Trading Strategy, Algorithmic Trading, S&P500, Python

Moving Average Crossover

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The moving average (MA) is a simple technical analysis tool that smooths out price data by creating a constantly updated average price. The average is taken over a specific period of time, like 10 days, 50 days, 200 days or any time period the trader chooses.

Moving average crossover is a strategy to apply when two moving averages on a chart: one longer and one shorter cross over each other. When the shorter-term MA crosses above the longer-term MA, it's a buy signal, as it indicates that the trend is shifting up. This is known as a "golden cross."

We will be using 10 day moving average as shorter-term MA and 50 day moving averages as longer-term MA.

aapl['MA10'] = aapl['Close'].rolling(10).mean()
aapl['MA50'] = aapl['Close'].rolling(50).mean()
aapl.head(2)

Here "aapl" is daily OHLC data of Apple stock.

The tutorial of how to use moving average is explained in the file 'notebook/Moving_average_crossover.ipynb'.

Python script where moving average crossover strategy is applied to all the data is given in Moving_average_crossover.py

Data Collection of S&P 500 stocks!

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For data collection we will be using yfinance module. To install it we will be using following bash command

pip install yfinance

yfinance need stock ticker symbol, start date and end data to download the OHLC, adusted close and volume data.

import yfinance
symbol = 'AAPL'
start_date = '2019-01-01'
end_date = '2020-06-01'
stock_data = yfinance.download( symbol, 
                                start = start_date, 
                                end = end_date, 
                                progress=False)

The tutorial of how to use collect stock data is explained in the file 'notebook/S&P500_data.ipynb'.

Python script where S&P500 data is downloaded for all the stock tickers is given here S&P500_data.py

Result

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Finally the result of net wealth and wealth ratio for all the stocks are save in Stock_gain_loss folder as csv files for given year.