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/

We develop a simple trading system exploiting the mean-reverting behaviour of the SP500 market index. To generate buy and sell signals, we will use simple moving averages as noise filters. Since we know that the SP500 is mean-reverting in a short term, we will use short-term moving averages.

http://tech.harbourfronts.com/mean-reverting-trading-system-quantitative-trading-in-python/

We are going to examine the mean-reverting and trending properties of SP500 directly using the autocorrelation functions. We do so with the goal of designing quantitative trading systems on stock indices.

http://tech.harbourfronts.com/autocorrelation-properties-of-sp500-quantitative-trading-in-python/

We discuss ways to determine the dividend yield accurately. We use traded options to determine the implied dividend yield. Specifically, if the options are of European-style exercise, then we can use the put-call parity to create a synthetic single stock future.

http://tech.harbourfronts.com/how-to-determine-implied-dividend-yield-derivative-valuation-in-excel/

We use the Exponential Weighted (EW) historical volatility that assigns bigger weights to the recent returns, and smaller weights to the past ones. The EWHV is more responsive than the equally weighted historical volatility. Also, the decline of the EWHV from its peak is smoother than that of the equally weighted HV.

http://tech.harbourfronts.com/exponentially-weighted-historical-volatility-in-excel-volatility-analysis-in-excel/

We are going to perform some numerical experiments. Specifically, we are going to use the portfolio optimization program developed in the previous post in order to study the effect of diversification.

http://tech.harbourfronts.com/modern-portfolio-theory-effect-of-diversification-on-the-optimal-portfolio-portfolio-management-in-python/

We are going to search for the optimal portfolio, i.e. one that has the highest risk-adjusted return. To do so, we will maximize the portfolio’s Sharpe ratio. The Sharpe Ratio is a financial metric that helps investors determine the return of an investment compared to its risk. The higher the Sharpe Ratio of a portfolio, the better it is in terms of risk-adjusted return.

http://tech.harbourfronts.com/modern-portfolio-theory-searching-for-the-optimal-portfolio-portfolio-management-in-python/

Harry M. Markowitz is the founder of Modern Portfolio Theory (MPT) which originated from his 1952 essay on portfolio selection. In this post, we are going to provide a concrete example of implementing MPT in Python. Our portfolio consists of 3 Exchange Traded Funds (ETF): SPY, TLT, and GLD which track the S&P500, long-term Treasury bond, and gold respectively.

http://tech.harbourfronts.com/trading/modern-portfolio-theory-portfolio-management-python/

Pair trading, or statistical arbitrage, is one of the oldest forms of quantitative trading. We are going to present some relevant statistical tests for analyzing the Australia/Canada pair. We chose this pair because these countries’ economies are tied strongly to the commodity sector, therefore they share similar characteristics and could be a good candidate for pair trading

http://tech.harbourfronts.com/trading/statistical-analysis-etf-pair-quantitative-trading-python/

We are a boutique financial service firm specializing in quantitative analysis, derivative valuation and risk management. Our clients range from asset management firms to industrial, non-financial companies. Our services include: Valuation of financial derivatives such as convertible bonds, mortgage backed securities, variance swaps, credit default swaps, collateral debt obligation

http://tech.harbourfronts.com/