zliangak/backtesting-rsi-algo

Backtesting an RSI Trading Algorithm with Quantopian Zipline and Pyfolio Python Libraries

Python RSI Momentum Trading Strategy Backtest with Zipline and Pyfolio

Use the RSI signal to drive algorithm buying and selling decisions. When the RSI is over 70, a stock is considered overbought and is sold. On the other hand, when the RSI is below 30, a stock is considered oversold and is bought. Here is additional information from Investorpedia.

Create and set-up a new virtual environment in Conda

Download and install Anaconda

Create a new environment in your conda navidagor or terminal and use Python version 3.5 given dependencies.

$ conda create -n env_zipline python=3.5

Activate the new environment.

$ conda activate env_zipline

Install the packages we need to run the strategy, backtest, and analyze the results

Install the Zipline backtesting library from Quantopian.

$ conda install -c Quantopian zipline

Install interactive Python shell and a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.

$ conda install -c anaconda ipykernel

Install the Python wrapper for TA-Lib

$ pip install TA-Lib

Install Pyfolio, see documentation here for more information.

pip install pyfolio

Get the stock pricing data necessary for the algo strategy

Obtain a free API key from Quandl

Set an environmental variable in your terminal with your API key.

$ export QUANDL_API_KEY='your API key here'

Ingest the Quandl Wiki Prices data into Zipline

$ zipline ingest -b quandl

Running the backtest

Run the below code in your terminal, check the Zipline Documentation for additional information.

$ zipline run -f backtest.py --start 2014-1-1 --end 2018-1-1 -o perf.pickle --no-benchmark --capital-base 20000 --bundle quandl