In a previous post, we demonstrated the mean-reverting and trending properties of SP500. We subsequently developed a trading system based on the mean-reverting behavior of the index. In this installment, we will develop a trend-following trading strategy.
http://tech.harbourfronts.com/trend-following-trading-system-quantitative-trading-in-python/
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In a previous post, we demonstrated the mean-reverting and trending properties of SP500. We subsequently developed a trading system based on the mean-reverting behavior of the index. In this installment, we will develop a trend-following trading strategy.
http://tech.harbourfronts.com/trend-following-trading-system-quantitative-trading-in-python/
Forecasting Implied Volatility with ARIMA Model-Volatility Analysis in Python
Harbourfront Technologies
2 minutes 7 seconds
5 years ago
Forecasting Implied Volatility with ARIMA Model-Volatility Analysis in Python
In a previous post, we presented theory and a practical example of calculating implied volatility for a given stock option. In this post, we are going to implement a model for forecasting the implied volatility. Specifically, we are going to use the Autoregressive Integrated Moving Average (ARIMA) model to forecast the volatility index VIX.
http://tech.harbourfronts.com/trading/forecasting-implied-volatility-arima-model-volatility-analysis-python/
Harbourfront Technologies
In a previous post, we demonstrated the mean-reverting and trending properties of SP500. We subsequently developed a trading system based on the mean-reverting behavior of the index. In this installment, we will develop a trend-following trading strategy.
http://tech.harbourfronts.com/trend-following-trading-system-quantitative-trading-in-python/
