How to calculate average drawdown of a trading system?
First of all, I think I'll clear off some confusion in the topic. The Sterling Ratio is a very simple investment portfolio measurement that fits nicely to the topic of personal finance, although not so much to a foreign exchange trading system. The Sterling Ratio is mainly used in the context of hedge funds to measure its risk-reward ratio for long term investments. To do so, it has been adapted to the following in order to appear more like the Sharpe Ratio:  I Suppose this is why you question the Average Largest Draw-down. I'll come back to that later. It's original definition, suggested by the company Deane Sterling Jones, is a little different and perhaps the one you should use if you want to measure your trading system's long term risk-reward ratio, which is as followed:  Note: Average Annual Draw-down has to be negative on the above-mentioned formula. This one is very simple to calculate and the one to use if you want to measure any portfolio's long-term results, such an example of a 5 or 10 years period and calculate the average of each years largest drawdown. To answer @Dheer's comment, this specific measurement can also be used in personal investments portfolio, which is considered a topic related to personal finance. Back to the first one, which answers your question. It's used in most cases in investment strategies, such as hedging, not trading systems. By hedging I mean that in these cases long term investments are made in anti-correlated securities to obtain a diversified portfolio with a very stable growth. This one is calculated normally annually because you rely on the Annual Risk-Free Rate. Having that in mind I think you can guess that the Average Largest Drawdown is the average between the Largest/Maximum Drawdown from each security in the portfolio. And this doesn't make sense in a trading system. Example: If you have invested in 5 different securities where we calculated the Largest Draw-down for each, such as represented in the following array: MaxDD[5] = { 0.12, 0.23, 0.06, 0.36, 0.09 }, in this case your Average Largest Draw-down is the average(MaxDD) that equals 0.172 or 17,2% If your portfolio's annual return is 15% and the Risk-free Rate is 10%, your Sterling Ratio SR = (0.15 - 0.10)/0.172, which result to 0.29. The higher the rate better is the risk-reward ratio of your portfolio. I suggest in your case to only use the original Sterling Ratio to calculate your long-term risk-reward, in any other case I suggest looking at the Sharpe and Sortino ratios instead.
Also, from personal experience, most banks' internal IT systems are bare minimum to keep working. Outside of trading systems the back office are often on Lotus Notes 99 or similar just because back office is seen as a cost and it's easy to justify not upgrading IT systems.  For sure, if the ROI is right it might get approved but it's hard to overstate how much time of your average back office employee is already spent fighting shitty systems that could be improved with today's tech. The major drawback to borrowing to invest (i.e. using leverage) is that your return on investment must be high enough to overcome the cost of finance. The average return on the S&P 500 is about 9.8% (from CNBC) a typical unsecured personal loan will have an interest rate of around 18-36% APR (from NerdWallet). This means that on average you will be paying more interest than you are receiving in returns so are losing money on the margin investment. Sometimes the S&P falls and over those periods you would be paying out interest having lost money so will have a negative return! You may have better credit and so be able to get a lower rate but I don't know your loan terms currently. Secured loans, such as remortgaging your house, will have lower costs but come with more life changing risks. The above assumes that you are getting financing by directly borrowing money, however, it is also possible to trade on margin. This is where you post a proportion of the value that you wish to trade with as collateral against a loan to buy the security. This form of finance is normally used by day traders and other short term holders of stocks. Although the financing costs here are low (I am not charged an interest rate on intraday margin trading) there are very high costs if you exceed the term of the loan. An example is that I am charged a fee if I hold a position overnight and my profits and losses are crystallised at that time. If I am in a losing position at that time the crystallisation process and fee can result in not having enough margin to recover the position and the loss of a potentially profit making position. Additionally if the amount of collateral cash (margin) posted is insufficient to cover the expected losses as calculated by your broker they will initiate a margin call asking for more collateral money. If you do not (or cannot) post this extra margin your losing position will be cashed out and you will take as a loss the total loss at that time. Since the market can change very rapidly, such as in a flash crash, this can result in your losing more money than you had in the first place. As this is essentially a loan you can be bankrupted by this. Overall using leverage to invest magnifies your potential profits but it also magnifies your potential losses. In many cases this magnification could be sufficient to lose you more money than you had originally invested. In addition to magnification you need to consider the cost of finance and that your return over the course of the loan needs to be higher than your cost of finance as well as inflation and other opportunity costs of capital. The S&P 500 is a relatively low volatility market in general so is unlikely to return losses in any given period that will mean that leverage of 1.25 times will take you into losses beyond your own capital investment but it is not impossible. The low level of risk automatically means that your returns are lower and so your cost of capital is likely to be a large proportion of your returns and your returns may not completely cover the cost of capital even when you are making money. The key thing if you are going to trade or invest on leverage is to understand the terms and costs of your leverage and discount them from any returns that you receive before declaring to yourself that you are profitable. It is even more important than usual to know how your positions are doing and whether you are covering your cost of capital when using leverage. It is also very important to know the terms of your leverage in detail, especially what will happen when and if your credit runs out for whatever reason be it the end of the financing period (the length of the loan) or your leverage ratio gets too high. You should also be aware of the costs of closing out the loan early should you need to do so and how to factor that into your investing decisions. "The formula for standard deviation is fairly simple in both the discrete and continuous cases. It's mostly safe to use the discrete case when working with adjusted closing prices. Once you've calculated the standard deviation for a given time period, the next task (in the simplest case) is to calculate the mean of that same period. This allows you to roughly approximate the distribution, which can give you all sorts of testable hypotheses. Two standard deviations (σ) away from the mean (μ) is given by:  It doesn't make any sense to talk about ""two standard deviations away from the price"" unless that price is the mean or some other statistic for a given time period. Normally you would look at how far the price is from the mean, e.g. does the price fall two or three standard deviations away from the mean or some other technical indicator like the Average True Range (an exponential moving average of the True Range), some support level, another security, etc.  For most of this answer, I'll assume we're using the mean for the chosen time period as a base. However, the answer is still more complicated than many people realize. As I said before, to calculate the standard deviation, you need to decide on a time period. For example, you could use S&P 500 data from Yahoo Finance and calculate the standard deviation for all adjusted closing prices since January 3, 1950. Downloading the data into Stata and applying the summarize command gives me: As you can probably see, however, these numbers don't make much sense. Looking at the data, we can see that the S&P 500 hasn't traded close to 424.4896 since November 1992. Clearly, we can't assume that this mean and standard deviation as representative of current market conditions. Furthermore, these numbers would imply that the S&P 500 is currently trading at almost three standard deviations away from its mean, which for many distribution is a highly improbable event. The Great Recession, quantitative easing, etc. may have changed the market significantly, but not to such a great extent.  The problem arises from the fact that security prices are usually non-stationary.. This means that the underlying distribution from which security prices are ""drawn"" shifts through time and space. For example, prices could be normally distributed in the 50's, then gamma distributed in the 60's because of a shock, then normally distributed again in the 70's. This implies that calculating summary statistics, e.g. mean, standard deviation, etc. are essentially meaningless for time periods in which prices could follow multiple distributions.  For this and other reasons, it's standard practice to look at the standard deviation of returns or differences instead of prices. I covered in detail the reasons for this and various procedures to use in another answer. In short, you can calculate the first difference for each period, which is merely the difference between the closing price of that period and the closing price of the previous period. This will usually give you a stationary process, from which you can obtain more meaningful values of the standard deviation, mean, etc.  Let's use the S&P500 as an example again. This time, however, I'm only using data from 1990 onwards, for the sake of simplicity (and to make the graphs a bit more manageable). The summary statistics look like this: and the graph looks like this; the mean is the central horizontal red line, and the top and bottom lines indicate one standard deviation above and below the mean, respectively.   As you can see, the graph seems to indicate that there were long periods in which the index was priced well outside this range. Although this could be the case, the graph definitely exhibits a trend, along with some seemingly exogenous shocks (see my linked answer).  Taking the first difference, however, yields these summary statistics: with a graph like this:  This looks a lot more reasonable. In periods of recession, the price appears much more volatile, and it breaches the +/- one standard deviation lines indicated on the graph.  This is only a simple summary, but using first differencing as part of the wider process of detrending/decomposing a time series is a good first step.  For some technical indicators, however, stationary isn't as relevant. This is the case for some types of moving averages and their associated indicators. Take Bollinger bands for instance. These are technical indicators that show a number of standard deviations above and below a moving average. Like any calculation of standard deviation, moving average, statistic, etc. they require data over a specified time period. The analyst chooses a certain number of historical periods, e.g. 20, and calculates the moving average for that many previous periods and the moving/rolling standard deviation for those same periods as well. The Bollinger bands represent the values a certain number of standard deviations away from the moving average at a given point in time. At this given point, you can calculate the value two standard deviations ""away from the value,"" but doing so still requires the historical stock price (or at least the historical moving average). If you're only given the price in isolation, you're out of luck.  Moving averages can indirectly sidestep some of the issues of stationarity I described above because it's straightforward to estimate a time series with a process built from a moving average (specifically, an auto-regressive moving average process) but the econometrics of time series is a topic for another day.  The Stata code I used to generate the graphs and summary statistics:" You can withdraw from CPP as early as 60. However, by doing so, you will permanently reduce the payments. The reduction is calculated based on average life expectancies. If you live for an average amount of time, that means you'll receive approximately the same total amount (after inflation adjustments) whether you start pulling from CPP at 60, 65, or even delay your pension later. People may have pensions through systems other than CPP. This is often true for big business or government work. They may work differently. People who retire at 55 with a pension are not getting their pension through CPP. A person retiring at 55 would need to wait at least five years to draw from the CPP, and ten years before he or she was eligible for a full pension through CPP. Canada also offers Old Age Security (OAS). This is only available once you are 65 years old or older, though this is changing. Starting in 2023, this will gradually change to 67 years or older. See this page for more details. As always, it's worth pointing out that the CPP and OAS will almost certainly not cover your full retirement expenses and you will need supplementary funds. You can make a rough calculation of the annual turnover rate of stocks by calculating the institutional investors holding of that stock. Institutional investors are the only firms that are required to provide such data. The good this is they usually make the lion share of trading activity. On the other hand, this task might proof arduous   A different ratio that could be used as a substitute Share Turnover which is calculated as:  Share Turnover gives the number of shares traded as a fraction of the number of shares outstanding. For example, if you compare the results of stock turnover for three companies and the results came as follows:  Company A-share turnover: 1.5 Times Company B share turnover: 3 times Company C share turnover: 0.3 times From the results, we can conclude that for a particular period, company C had the least activity and the number of shares traded for that period was only a small fraction of the shares outstanding while other traders of company C hold most shares and never trade them. If you make a cross sectional analysis of a list of businesses you intend to invest in, you could figure which one has the least number of rapidity in the shares traded. Something like cost = a × avg_spreadb + c × volatilityd × (order_size/avg_volume)e. Different brokers have different formulas, and different trading patterns will have different coefficients. "The dow jones is an index of 30 stocks that's weighted based on the price per share of these stocks. To calculate the DJIA, the sum of the prices of all 30 stocks is divided by a divisor, the Dow Divisor. The divisor is adjusted in case of stock splits, spinoffs or similar structural changes, to ensure that such events do not in themselves alter the numerical value of the DJIA.  Prices are the result of supply and demand. Demand is defined as simply as ""I want this badly enough to pay cash for it"", ""supply is ""I own this and don't want to own it, therefore I'll sell it""  When investors go about deciding they'd like to buy some stock from people who own it and are wanting to sell it, a market is generated and a price both are willing to sell/buy at is found. If the price is too high/low, you won't sell or buy. The price has to be ""right"" based on your own personal valuation.   Since these trades are done through an exchange and are so common, literally millions of shares are traded per day, you get the best price available at that moment for your shares. If person A is only willing to give you 100 dollars and person B is willing to give you 100.01, screw person A. I'm selling shares to person B.  When this done on a macro-level (millions of shares traded per day) the exchanges will track and make public the ""price movement"" of what the market is willing to give for each and every publicly traded stock.  TLDR: SUPPLY AND DEMAND. DOW USES THEIR OWN METHODS." This would be a nice Raspberry Pi project for Mathematica, which comes bundled free on the Raspbian OS. You can program it up and leave it running.  It's not expensive and doesn't use much power. A program to monitor stock prices or volume could be written as simply as :-  This checks the volume of trades of Oct 2014 US crude oil futures every 30 seconds and sends an email if the volume jumps by more than 100. The financial data in this example is curated from Yahoo.  If specific data is not available or not updated frequently enough, if you can find an alternative online data source it's usually possible read the data in.  For example, this is apparently real-time data :- {Crude Oil, 92.79, -0.67, -0.71%} After leaving the above program running while writing this the volume of trades has risen like so :-  Edit I just set this running on a Raspberry Pi.  I had to use gmail for the email setup as described in this post: Configuring Mathematica to send email from a notebook.  Anyway, it's working.  Hope I don't get inundated with emails. ;-)