Sharpe ratio forex trading

Check out my ebook on quant trading where I teach you how to build profitable systematic trading strategies with Python tools, from scratch. Take a look at my new ebook on advanced trading strategies using time series analysis, machine learning and Bayesian statistics, with Python and R. When carrying out an algorithmic trading strategy it is tempting to consider the annualised return as the most useful performance metric.

However, there are many flaws with using this measure in isolation. The calculation of returns for certain strategies is not completely straightforward.

This is especially true for strategies that aren't directional such as market-neutral variants or strategies which make use of leverage. These factors make it hard to compare two strategies based solely upon their returns.

In addition, if we are presented with two strategies possessing identical returns how do we know which one contains more risk? Further, what do we even mean by "more risk"?

In finance, we are often concerned with volatility of returns and periods of drawdown. Thus if one of these strategies has a significantly higher volatility of returns we would likely find it less attractive, despite the fact that its historical returns might be similar if not identical.

These problems of strategy comparison and risk assessment motivate the use of the Sharpe Ratio. William Forsyth Sharpe is a Nobel-prize winning economist, who helped create the Capital Asset Pricing Model CAPM and developed the Sharpe Ratio in later updated in The ratio compares the mean average of the excess returns of the asset or strategy with the standard deviation of those returns.

Thus a lower volatility of returns will lead to a greater Sharpe ratio, assuming identical returns.

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The "Sharpe Ratio" often quoted by those carrying out trading strategies is the annualised Sharpe , the calculation of which depends upon the trading period of which the returns are measured.

Note that the Sharpe ratio itself MUST be calculated based on the Sharpe of that particular time period type. The formula for the Sharpe ratio above alludes to the use of a benchmark. A benchmark is used as a "yardstick" or a "hurdle" that a particular strategy must overcome for it to worth considering. The choice of benchmark can sometimes be unclear.

Why not the Russell ? Equally should a hedge fund strategy be benchmarking itself against a market index or an index of other hedge funds? There is also the complication of the "risk free rate". Should domestic government bonds be used? A basket of international bonds? Short-term or long-term bills?

Clearly there are plenty of ways to choose a benchmark! The Sharpe ratio generally utilises the risk-free rate and often, for US equities strategies, this is based on year government Treasury bills. In one particular instance, for market-neutral strategies, there is a particular complication regarding whether to make use of the risk-free rate or zero as the benchmark.

The market index itself should not be utilised as the strategy is, by design, market-neutral. The correct choice for a market-neutral portfolio is not to substract the risk-free rate because it is self-financing.

Hence there is no actual subtraction of the risk-free rate for dollar neutral strategies. Despite the prevalence of the Sharpe ratio within quantitative finance, it does suffer from some limitations. Firstly, the Sharpe ratio is backward looking. It only accounts for historical returns distribution and volatility, not those occuring in the future. When making judgements based on the Sharpe ratio there is an implicit assumption that the past will be similar to the future.

This is evidently not always the case, particular under market regime changes. The Sharpe ratio calculation assumes that the returns being used are normally distributed i.

Unfortunately, markets often suffer from kurtosis above that of a normal distribution. Essentially the distribution of returns has "fatter tails" and thus extreme events are more likely to occur than a Gaussian distribution would lead us to believe.

Hence, the Sharpe ratio is poor at characterising tail risk. This can be clearly seen in strategies which are highly prone to such risks. For instance, the sale of call options aka "pennies under a steam roller". A steady stream of option premia are generated by the sale of call options over time, leading to a low volatility of returns, with a strong excess above a benchmark.

In this instance the strategy would possess a high Sharpe ratio based on historical data. However, it does not take into account that such options may be called , leading to significant and sudden drawdowns or even wipeout in the equity curve. Hence, as with any measure of algorithmic trading strategy performance, the Sharpe ratio cannot be used in isolation.

Although this point might seem obvious to some, transaction costs MUST be included in the calculation of Sharpe ratio in order for it to be realistic. There are countless examples of trading strategies that have high Sharpes and thus a likelihood of great profitability only to be reduced to low Sharpe, low profitability strategies once realistic costs have been factored in.

This means making use of the net returns when calculating in excess of the benchmark. Hence, transaction costs must be factored in upstream of the Sharpe ratio calculation. One obvious question that has remained unanswered thus far in this article is "What is a good Sharpe Ratio for a strategy? The Sharpe ratio will often increase with trading frequency.

Some high frequency strategies will have high single and sometimes low double digit Sharpe ratios, as they can be profitable almost every day and certainly every month.

These strategies rarely suffer from catastrophic risk and thus minimise their volatility of returns, which leads to such high Sharpe ratios.

This has been quite a theoretical article up to this point. Now we will turn our attention to some actual examples. We will start simply, by considering a long-only buy-and-hold of an individual equity then consider a market-neutral strategy.

Both of these examples have been carried out in the Python pandas data analysis library. The first task is to actually obtain the data and put it into a pandas DataFrame object. In the article on securities master implementation in Python and MySQL I created a system for achieving this.

Alternatively, we can make use of this simpler code to grab Yahoo Finance data directly and put it straight into a pandas DataFrame. At the bottom of this script I have created a function to calculate the annualised Sharpe ratio based on a time-period returns stream:. Now that we have the ability to obtain data from Yahoo Finance and straightforwardly calculate the annualised Sharpe ratio, we can test out a buy and hold strategy for two equities.

We will use Google GOOG and Goldman Sachs GS from Jan 1st to May 29th when I wrote this article! We can create an additional helper function that allows us to quickly see buy-and-hold Sharpe across multiple equities for the same hardcoded period:. For Google, the Sharpe ratio for buying and holding is 0.

For Goldman Sachs it is 0. Now we can try the same calculation for a market-neutral strategy. The goal of this strategy is to fully isolate a particular equity's performance from the market in general. The simplest way to achieve this is to go short an equal amount in dollars of an Exchange Traded Fund ETF that is designed to track such a market.

To calculate the annualised Sharpe ratio of such a strategy we will obtain the historical prices for SPY and calculate the percentage returns in a similar manner to the previous stocks, with the exception that we will not use the risk-free benchmark. We will calculate the net daily returns which requires subtracting the difference between the long and the short returns and then dividing by 2, as we now have twice as much trading capital. Despite the Sharpe ratio being used almost everywhere in algorithmic trading, we need to consider other metrics of performance and risk.

In later articles we will discuss drawdowns and how they affect the decision to run a strategy or not. QuantStart Log In Sign Up.

Sharpe Ratio

Learn about QuantStart Read our Books Browse the Articles List Explore the Reading List Backtest with QSTrader Query the Support Knowledge Base. Sharpe Ratio for Algorithmic Trading Performance Measurement. By Michael Halls-Moore on May 29th, When carrying out an algorithmic trading strategy it is tempting to consider the annualised return as the most useful performance metric. Definition of the Sharpe Ratio William Forsyth Sharpe is a Nobel-prize winning economist, who helped create the Capital Asset Pricing Model CAPM and developed the Sharpe Ratio in later updated in Benchmark Inclusion The formula for the Sharpe ratio above alludes to the use of a benchmark.

Understanding The Sharpe Ratio

Limitations Despite the prevalence of the Sharpe ratio within quantitative finance, it does suffer from some limitations. Practical Usage and Examples One obvious question that has remained unanswered thus far in this article is "What is a good Sharpe Ratio for a strategy? Examples of Sharpe Ratios This has been quite a theoretical article up to this point. At the bottom of this script I have created a function to calculate the annualised Sharpe ratio based on a time-period returns stream: Yahoo Finance ticker symbol, e.

End date in YYYY, M, D format """ Construct the Yahoo URL with the correct integer query parameters for start and end dates. Note that some parameters are zero-based! N defaults to , which then assumes a stream of daily returns. The function assumes that the returns are the excess of those compared to a benchmark.

We can create an additional helper function that allows us to quickly see buy-and-hold Sharpe across multiple equities for the same hardcoded period: The dates have been hardcoded here for the QuantStart article on Sharpe ratios.