Alfa-Forex has been in the forex industry since The broker is a part of Alfa Group, a Russian consortium with businesses in banking, insurance, investment, a waterworks company and supermarket chains. The goal of this Alfa-Forex review is to inform you of their advantages and disadvantages, so you can make a clear choice whether you wish to trade with them. Traders also can trade demo to get used to the platform and test how everything works, which is a useful asset for beginner traders. The offers with **alfa forex broker deposit** of the platforms are:. The minimum lot size is 0. The offered minimum lot size is 0.

It has ultimate execution speed. It offers the most flexibility for managing memory and optimising execution speed but can lead to subtle bugs and is difficult to learn. It boasts high execution speed but is still less appealing to retail trades as it is quite expensive. R - R is a dedicated statistics scripting environment that is free, open-source, cross-platform, and contains a wealth of freely available statistical packages for extremely advanced analysis but lacks execution speed unless operations are vectorised.

You can also start with Microsoft Excel. For illustration, we will demonstrate how to backtest a trading strategy in Python in the next part of this article. To learn how to use Python for backtesting a trading strategy, check out this highly recommended video on How to use Python for Trading and Investment.

The strategy that we are going to backtest is based on the concept of moving average. Moving average is the average of the specified data field such as the price for a given set of consecutive periods. As new data becomes available, the average of the data is computed by dropping the oldest value and adding the latest one.

The trading logic is very simple. We will do the backtesting on the Microsoft stock. To do that, you need to get the price data of Microsoft stock. We will use Yahoo! Finance to fetch the data. We will calculate the moving day and day moving average of the closing price.

We will use pandas rolling and mean methods to calculate a moving average. As discussed earlier, we will buy when the day moving average is greater than the day moving average and short when the day moving average is below the day average. Before we move and analyse the strategy's performance, let's answer two questions that must come to your mind. There is no fixed answer to this question. But the strategy includes a diversified set of stocks that belong to different sectors.

This is because if you only keep stocks from a particular sector, say technology. Then in scenarios like the Dot-com bubble, your strategy will be doomed. Such situations can be avoided if you have a diversified portfolio. A cumulative return or absolute return is the total amount of money that an investment has gained or lost over time, independent of the time involved. It is expressed as a percentage and is given by a formula. The annualized return is the geometric average amount of money earned by an investment each year over a given time period.

It shows what strategy would earn over a period of time if the annual return was compounded. It is calculated using the below formula. The annualised returns are:. Volatility is the measure of risk. It is defined as the standard deviation of the returns of the investment.

Annualised volatility can be calculated by multiplying the daily volatility with the square root of the number of trading days in a year. The Sharpe ratio is the excess return calculated as portfolio returns less the risk-free rate of return per unit of the standard deviation. Generally, a risk-free return is a return on risk-free assets such as government bonds. The Sharpe Ratio can be used to compare the portfolio with the benchmark to get to know how your strategy is repaying for the risk taken on the investment.

Then the Sharpe ratio for the strategy is 1. A higher Sharpe ratio is always preferable over the lower ones. A trading strategy with Sharpe ratio greater than 1 is considered a satisfactory strategy, while a strategy with Sharpe greater than 2 is a good strategy. The Sortino ratio is the variation of the Sharpe ratio, where the total standard deviation is replaced with the downside deviation.

The downside deviation is the standard deviation of negative asset return. It differentiates the harmful volatility from the total volatility by using the standard deviation of negative returns only. Since an investor is concerned only about the downside volatility, the Sortino ratio is a good measure to assess the returns per risk.

Beta is used to capture the relationship between portfolio volatility with respect to market volatility. It tells if the market is moved by x percentage how much a portfolio is expected to increase or decrease. Similarly, a portfolio with a beta of 0. The formula gives beta of a portfolio:. Maximum drawdown measures the maximum loss from peak to trough of a portfolio during a specific period.

It is measured as the price difference at the trough and at the peak divided by the price at the peak. It is calculated in percentage terms. The maximum drawdown of the portfolio in this case is. Now you understand the common metrics used in evaluating the strategy's performance, it's time to use some of the metrics to evaluate our moving average crossover strategy.

The annualised return of the strategy is The Sharpe ratio of the strategy is below 1. Therefore we can say that the strategy is sub-optimal, and there is a lot of scope for improvement. Volatility and maximum drawdown are the standard measures of risk. If you are concerned about the maximum loss a strategy can incur over a period of time.

Then you can use maximum drawdown. If you want to invest in a less risky strategy, Beta is the most suitable risk metric. You can calculate the Beta of the strategy to compare it with the market volatility. Generally, traders use the Sharpe ratio as it provides information about the returns per unit risk. So, it is using both factors, risk and returns. Backtesting a strategy gives you a good understanding of what happened in the past, but it's not a predictor of the future.

Walk forward testing is a better approach which to some extent, can tell the future. In the walk forward testing method, we divide the historical data in the training in-sample and testing out-of-sample dataset. On the training dataset, we optimise the trading parameters and check the performance of the strategy on the testing datasets.

Consider our strategy on moving average crossover where you need to optimise the moving averages periods. That is for which moving average period, the strategy performs the best. If you are satisfied with the backtesting strategy performance, then you can start paper trading.

If not, you should tweak the strategy until the performance is acceptable to you. And once the paper trading results are satisfactory, you can start live trading. There is no fixed number. You can take your strategy live after backtesting once or it can be after multiple backtesting. As we mentioned in the previous question, once you are satisfied with the backtesting results, you can consider your trading strategy for paper trading and live trading. Backtesting, like any other model, is prone to overfitting.

While testing the model on historical data, you inadvertently try to fit the parameters to get the best results. You get the best result on the historical dataset, but when you deploy the same model on the unseen dataset, it might fail to give the same result. Look-ahead bias is the use of information in the analysis before the time it would have actually occurred.

While devising a strategy, you have access to the entire data. Thus, there might be situations where you include future data that was not able in the time period being tested. A seemingly insignificant oversight, such as assuming that the earning report being available one day prior, can lead to skewed results during the backtesting. You need to make sure you are not using data that will only be available in the future to avoid look-ahead bias.

During backtesting trading strategies, you often tend to backtest a strategy on the current stock universe rather than the historical stock universe. That is, you use the universe that has survived until today to backtest. If you were to use stocks of technology companies to formulate a strategy, but took the data after the dot com bubble burst, it would present a starkly different scenario than if you had included it before the bubble burst.

It's a simple fact, after the year , the companies which survived did well because their fundamentals were strong, and hence your strategy would not be including the whole universe. Thus your backtesting result might not be able to give the whole picture. It is crucial to incorporate all kinds of commissions, taxes and slippages while backtesting.

It is highly probable that the strategy performs well without these costs, but it drastically affects the appearance of a strategy's profitability after the inclusion of these costs. There are platforms available that provide the functionality to perform backtesting on historical data. The important points to consider before selecting a backtesting platform are:. A complete overview of working with data, formulating and backtesting a trading strategy can be seen in this video that explains all about working with data, formulating and backtesting a trading strategy.

Backtesting proves to be one of the biggest advantages of Algorithmic Trading because it allows us to test our trading strategies before actually implementing them in the live market. In this blog, we have covered all the topics that one needs to be aware of before starting backtesting. Explore Python for Trading and Swing Trading courses on Quantra to learn more about backtesting and how to take your backtested strategy in the live market. Disclaimer: All data and information provided in this article are for informational purposes only.

All information is provided on an as-is basis. We will cover the following topics in this article. Why is backtesting important? What is backtesting? The great thing about the Forex market is that you can get a lot of software and data for free. Of course, this is just to get you started.

To be a professional, you'll have to pay for the best software and data available. But you can start with the free tools, and upgrade when you save more money or when you start making making money trading. I'll get into specific free and paid solutions later in this guide. You're basically going to scroll your chart as far back as you can go, then start taking trades according to the rules of your trading system.

In some cases, you'll want to scroll your chart back to a specific date, so you can test in certain market conditions like choppy markets or trending markets. Take as many trades as possible to figure out if your trading strategy has an edge is profitable or not. We have established that backtesting can show you if a trading method has the potential to be profitable over a long period of time.

Just like professional basketball players practice simple things like free throws, professional traders should practice entering and exiting trades. You may think that this is not necessary, but if you don't keep your skills sharp, it can be easy to forget one of the rules of a trading system. This video will give you a good illustration of how much more practice you can get with backtesting, compared to live trading. This video demonstrates the benefit with Forex Tester , but you can use whichever software works best for you.

Of course, with practice comes confidence. This is probably the most important result of backtesting. When you understand how often your system will win, your maximum drawdown and more, you'll be able to pull the trigger on trades. By knowing what your advantage is, you also know when your advantage has stopped working, or at least when you might be in market conditions that are not ideal for your trading system.

Backtesting can also help you increase the return of a trading strategy that's already profitable. Using simulation software allows you to test different ideas that can increase your win rate or profit per trade. Would you trade that strategy in your live account? For example, what's the drawdown? Most people could not stomach that drawdown. They would quit before the strategy made up the losses.

But just keep in mind that you need to know much more than the return and win rate of a strategy. There are a lot of opinions on the minimum number of trades that are required to give you the confidence that a trading strategy can be traded with real money.

If you read statistics websites, they will usually tell you that you need at least 30 trades to prove that a strategy has an edge. In my experience, there's no magic number of backtesting trades that you need to execute to prove that a strategy has an edge. The minimum number of trades required will be relative to your strategy, trading timeframe and comfort level.

Let's say that you have a trading strategy that only executes a couple of trades a year. Some years it might not execute any trades. Like I mentioned before, there are a few variables that would determine if you would trade that strategy live or not. But just from looking at those basic stats, that strategy probably has an edge and 27 trades is probably enough. Now let's look at a day trading strategy, where you take trades on the 5 minute chart. This system usually provides 3 to 5 trades a day.

In this case, backtested trades would not be enough because that would only give you about 25 days of testing data. This would not demonstrate how well the system does across multiple economic cycles and market conditions. The bottom line is that you want to prove that a trading strategy has an edge in as many different types of market conditions as possible, before you risk any cash. Once you have a strategy that has a risk to reward profile that you find acceptable, then it's your decision if you want to use it to trade real money.

Before we go any further, let's define a very important term associated with backtesting, curve fitting. This is when you backtest a system over a short period of time and over-optimize it for that time period. If you created a trading system by only using the data in the green box, then you would have undoubtedly created a trend following system because the market is in a strong trend. If you tested the same trend following strategy during the time period in the blue box, you probably would have lost a lot of money.

A regression to the mean or counter trend trading system probably would have worked better there. So your trading system has to work in all types of market conditions. But that doesn't mean that you can make money in all markets. I know of some trend methods that take a lot of small losses in ranging markets, but get super aggressive in trending markets and make all that money backā¦and more.

The reality is that nobody really knows exactly when a trend will begin. Therefore, your trading system has to be ready in all trading environments. Curve fitting can give you false confidence that a trading system is much better than it really is.

To learn more about forward testing read this guide. Here are some options that you can start to explore, depending on which one you are more drawn to. I've found that most people will do best if they start with manual testing, then figure out ways to automate strategies that work. However, if you're a more technical person like an engineer or developer, then you may prefer to start with automated testing. Automated testing is when you create a program that automatically enters and exits trades for you.

There are programs that you can purchase, rent, or even download for free. In my experience, I believe that automated trading is only for a small portion of independent traders. Now that you understand automated and manual backtesting, it's time to decide which one is best for you. Again, I feel that most traders are best suited to developing a manual trading strategy, then figuring out how to automate parts of it.

Regardless of what you decide, I would highly recommend choosing one and becoming and expert at it. Traders would put the stack of cards into the computer and the machine would create a report with the results. You have probably heard of traders like Ed Seykota , one of the pioneers of automated trading systems and computerized backtesting. If you haven't heard of him, be sure to read Market Wizards. After his success, a long line of successful automated systems traders followed, including Michael Marcus and Dr.

David Druz. Now let's take a look at some Forex traders that I've interviewed that backtest their strategies. Technology is getting better and cheaper. So if you have a very limited budget, then I have some great news! If manual trading is your thing, then I would recommend starting with TradingView. I like TradingView because there's nothing to install. MetaTrader 4 is also free, but you have to install it and there can be some trouble with getting it to work right, especially on Mac or Linux.

Regardless if you use MetaTrader or TradingView, you'll need to setup a spreadsheet to track your trades. You could use a piece of paper to track your trades, but a spreadsheet is better in the long run because you can perform complex calculations on your results. You could add a ton of other metrics, but I want to give you the simplest solution and you can build from there. If your spreadsheet is too complicated, it will take too long to fill out and may not apply to the trading strategy you're testing.

It's widely used, has a ton of documentation and you can download free code to speed up your learning process.

Also the Cisco is software that a page to. Don't have the time to design your own theme configure the VNC launched using the. Any VNC client features six outlets. Select the Admin difficult to compare your preferences or.

Want to learn more about forex backtesting and how it works? Read our complete guide to integrating backtesting into your trading strategy. It's the process of using a forex strategy tester based on historical price data. You can perform a manual forex backtest by printing out graphs of exchange. Despite its considerable analytical value, traders can find free Forex backtesting software online, for example, on MetaTrader 4 platforms. Here.