Hackernoon logoHow To Get Started Doing Fast Backtesting Using Algo Trading Tools by@mikhailkirilin

How To Get Started Doing Fast Backtesting Using Algo Trading Tools

Algorithmic trading is a very efficient and profitable way of trading, expanding the possibilities of obtaining stable profits at a consistent rate by using complex and precise settings that are not affected by human errors. Backtesting assesses the success rate of a trading strategy by testing how it would have played out in retrospect using historical data. Algo trading is used in many forms of investment and trading such as: mid-term investors, buy-side firms (insurance companies, pension funds, mutual funds) and short-term traders.
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@mikhailkirilinMikhail Kirilin

Copywriter, community manager, editor. Interested in fintech, investing, fund management.

Algorithmic trading is a very efficient and profitable way of trading, expanding the possibilities of obtaining stable profits at a consistent rate by using complex and precise settings that are not affected by human errors. Algo trading helps you grow your hedge fund with ease.

But as with any piece of software, it can go wrong. No one would want to risk a big amount of capital in a program that is not vetted. Thankfully we don’t have to, since there are plenty of platforms that we can use to backtest our strategies before putting our money into them.

Why Algorithmic Trading

An algorithm is defined by a specific set of instructions aimed to carry a process or a task. There are cases when a human trader is not that efficient or fast at handling a big number of trades, and that’s why we need the help of an intelligent algorithm.

Trading algorithms have gained a lot of popularity in recent years and many big clients use them. These mathematical algorithms analyze every trade and quote in the stock market, being able to precisely make intelligent trading decisions based on predefined settings. Computer-directed trading or algo trading cuts down transaction costs, and allows investment managers to take full control of their trading processes. The innovations in algorithmic trading offer continued returns for fund managers with the ability to easily absorb the costs.

Any strategy for algo trading needs an identified opportunity, which in time turns out to be profitable by improving the earnings. The algorithmic strategies follow a defined set of rules, based on price, timing, quantity or any other mathematical model. Besides the benefit of continuous profit opportunities for the fund, algorithmic trading also makes the trading more systematic by ruling out human emotions, which can affect even the best fund manager.

You can use complex instructions to set up an algorithm to do the trading for your fund or simply use one that is already made. The program will perform all the complex trades automatically without any intervention, according to the conditions coded in it.You no longer need to monitor live graphs and prices and put in orders manually. The algorithmic trading system will do all the work for you.

Algo-trading is used in many forms of investment and trading such as:

  • Mid to long-term investors or buy-side firms (insurance companies, pension funds, mutual funds);
  • Short-term traders and sell-side participants;
  • Systematic traders as hedge funds, trend followers, or pairs traders.

Algorithmic trading brings a lot of benefits:

  1. Trades are timed instantly and correctly to avoid important price changes.
  2. Reduced risk of manual errors.
  3. Trades are executed at the best price possible.
  4. Trade orders are placed instantly and accurately.
  5. Reduced transaction costs.
  6. No risk of mistakes based on emotional factors.
  7. Concurrent automated checks on different market conditions.
  8. The possibility of performing high-frequency trading (HFT), which is not possible manually. This trading method capitalizes on placing a large number of orders at high speeds, across multiple markets and decision parameters.
  9. Algo-trading strategies can be tested using real-time or historical data to see if they are a viable trading strategy before putting them to work with money.

What is Backtesting?

Backtesting assesses the success rate of a trading strategy by testing how it would have played out in retrospect using historical data. The theory behind it is that any strategy that worked well in the past will also work well in the future. Conversely, any strategy that performed poorly in the past is likely to also perform poorly in the future.

Backtesting allows us to simulate and test an algorithmic strategy by generating results with the help of historical data. This allows the trader to analyze the risk and profitability before risking any capital.

If backtesting is done properly, the fund manager is assured that the algorithmic strategy tested is viable and likely to yield profits when implemented with capital. Backtesting also allows the trader to identify strategies that are suboptimal, and therefore need to be modified or rejected.

Strategies used by automated trading systems rely heavily on backtesting to prove their worth. This is a very important step, especially when managing a big capital.

When testing a strategy on historical data, it is beneficial to reserve a time period for testing purposes. If the strategy will prove successful, testing it on alternate time periods can help confirm its viability and success-rate.

The 3 Aims of Backtesting

Backtesting is done to accomplish 3 things:

  • To show if a strategy performs well when it’s supposed to and vice versa.
  • To show how the strategy performs in different markets.
  • To provide insights on how the algorithmic strategy can be improved.

1. Performance during selected periods

With the help of backtesting we can check if a strategy is making money when it’s supposed to and loses money when it should.

For example, let’s assume one of our strategies is expected to perform better in times when the markets are volatile. If our backtesting shows that we make more money than expected in less volatile periods of time, this is an issue, because the algorithm is not performing as it should.

This red flag means we should analyze our strategy and determine what went wrong.

2. How the strategy performs in different markets

To determine the consistency of profitability for our strategy, backtesting can be performed in different market environments.

That means we can run backtests with other market assets or different stocks.

Another way of testing different markets is by comparing different time periods, especially periods when there are obvious market trends vs. periods when there aren’t.

If a strategy performs well in different circumstances, that means you can be much more certain about the positive results it yields.

3. Improving the strategy

After you perform a backtest, you can make improvements to the strategy by looking at the results. A strategy can be therefore improved and reanalyzed until the trader is satisfied with the results.

This is a very important benefit of backtesting, and it allows us to identify mistakes and errors that would otherwise be too costly when the strategy is applied to capital.

However, there is a downside to it. A common pitfall here is to continuously tweak it, hoping that you will get better results in the backtest. This is called overfitting, and it rarely leads to profitability when you apply the strategy to real money.

Why is Backtesting Important to You?

Backtesting is an important element for effective algorithmic trading. By generating results with historical data, strategies can be tested and verified before applying them to capital.

Hedge funds are focused on generating profit, and backtesting is one of the best ways to make sure that a specific algorithmic strategy will increase your ROI.

Algorithmic trading was implemented to offer traders an edge on the market. A well-designed trading strategy will provide constant profit in a reasonable timeframe. But traders need an efficient way of designing trading strategies that will perform well in all types of market situations.

Since algorithmic trading usually involves a much higher number of trades than you would manually do, that also means the learning curve would have been costly if you had to do it on capital.

Backtesting helps to minimize the learning curve by testing the trading algorithm with historical data of the market. No matter if you code your own algorithm or you buy it from a trading algorithm market, you should always test it to check if it will be profitable for your fund.

Backtesting will give you the confidence and peace of mind that your trading strategy will yield positive results.

Best Automated Trading Solution For Simple Backtesting

MetaTrader 5 is one of the best backtesting tools that you can find for hedge funds. Besides backtesting, you can easily set up management, financial instruments and separate access for your employees and investors. It is an all-in-one platform to establish and automatize the fund in 5 minutes.

MetaTrader has one of the largest specialized communities that has grown over a decade. You can find a wealth of information on algorithmic trading and many professional groups ready to assist you. There’s no bigger community of people that use algorithmic trading elsewhere.

The Strategy Tester allows you to test algorithmic strategies before implementing them in trading. You can analyze and test a strategy with different settings and see which setting provides the most optimal results.

The backtesting tool is easy to use and it provides detailed and useful reports. Testing the performance of trading robots is one of the most important features of the platform, allowing you to see if the robots you bought from the MQL5 market or the ones that you build are profitable or not: there are over 13,000 ready-made solutions you can access.

Backtesting the algorithm strategies is even more reliable when you combine it with forward testing, a feature that can be accessed as easily on MetaTrader. The MQL5 Cloud Network grants access to thousands of agents around the world to increase your computation power, thus being able to run comprehensive tests in a short time, without needing a very powerful computer. The MQL5 language supports R and Python libraries.

Combined with the powerful hedge fund tools the platform provides, MetaTrader 5 is one of the best choices for managing and growing your fund with the power of algorithmic trading.

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