Every trader dreams of creating a profitable system, one that can deliver reliable returns with minimal risk. But how do you know when you’ve built a winning strategy? Automated Trading Systems (ATS) can help take your trading to the next level by removing emotional decision-making and improving execution speed. For those aiming for maximum efficiency, low latency trading can be a key component, enabling the system to execute trades faster than ever. But before you jump into the live markets, you’ll want to test your ATS and its underlying trading strategies thoroughly. This is where backtesting comes in. Backtesting trading strategies can help you understand how a system would have performed historically, giving you key insights that can help you refine your strategy before applying them in live conditions. This article will explore the ins and outs of backtesting trading strategies, including the best tools and how to get started.
One of the best tools for backtesting your trading strategies is QuantVPS’s trading VPS. A trading VPS gives you the resources you need to run your backtests effectively and efficiently so you can get reliable results and get back to trading.
What is a Backtesting Trading Strategy?
Backtesting trading strategy refers to evaluating a trading strategy’s performance using historical data to simulate how it would have performed in the past. Backtesting can be like a time machine for traders that allows them to go back to see how a strategy worked in different market conditions before committing real money. In your day-to-day life, before you buy anything like:
- Mobile phone
- House
- Car
You should check its features and history to determine if it is worth your money. The same applies to trading, but you can check your trading strategies here. With a wide range of markets to trade, you need to backtest your trading strategies to be sure they work.
A backtest is a tool that small retail traders and big institutions use. The world’s most successful hedge fund, Jim Simons’ Medallion Fund, uses backtesting continuously to develop new strategies. Why? Because backtesting a trading strategy works!
Backtesting a trading strategy works because you can:
- Falsify or confirm a trading idea
- Automate all your trading based on the backtests
- Exploit the law of large numbers, limit behavioral mistakes
- Save a lot of time in executions
Backtesting is a good use of time! We have done backtesting daily for over 20 years, and this article summarizes the main reasons why you should backtest and why it works.
What Goes Down in a Backtest?
So, what exactly is a backtest? A backtest is a way of testing a trading strategy on historical data to determine its performance. It simulates the strategy’s historical performance using historical data before committing real funds to it in live trading.
Backtesting trading strategies is based on the assumption that if the strategy performed well in a particular market previously, it has a good chance of working again. On the flip side, if the strategy did not perform well in the past for that market, it may not work well in the future.
So, let’s say you have a swing trading strategy that says that if the S&P 500 Index has a positive return in the past month, it will give a positive return over the next week. You want to test that theory to determine whether it is true. While you can forward-test it by using a demo account and waiting for the positive months to come (you check how the market performs in the month following each one), that would be very time-consuming.
Backtesting Your Strategy
A faster way to test your theory is to test it on historical price data. You go into the past to find all the times the market has a positive monthly return and then check how the market performed in the following week or month. If the performance of historical data is good, you might assume that the strategy will perform well in the current market, but if the performance is poor, you may discard the strategy or tweak and re-test it.
The Importance of Backtesting
You can backtest any period you like; the point is to measure how your predictions on past data work on future unknown data. Thus, backtesting is a crucial step in creating a trading system. It can help traders test, optimize, and improve their strategies, giving them the confidence to apply the approach in live trading.
A backtest has strict rules for when to buy and when to exit. In other words, you can code the strategy and find out with 100% certainty how the strategy has performed in the past. Thus, this is a backtest on historical data and strict trading rules.
That is why it’s called a back test (history). We can argue it’s a kind of quantified technical analysis–technical analysis backtesting. Of course, this doesn’t give any certainties about the future, but you know if the strategy has performed well or poorly in the past.
If something has performed poorly, it’s unlikely to perform well. Nevertheless, the ever-changing market cycles make strategies perform well in specific markets and poorly in others. This is the reality of trading.
On the other hand, if a backtest proves that your idea has worked well in the past, it most likely will perform better than any idea that has performed poorly. Of course, a positive backtest is no guarantee that it will work in the future, but we believe it’s the best indication you can get.
Why You Should Backtest Your Trading Strategies
You should backtest a trading strategy to determine its positive expectancy, improve it, or determine its correlation to other trading strategies.
Let’s break it down:
Many traders, including experienced ones, lose money—not because they don’t understand how the market works but because their trading strategies lack a statistical edge in that market.
No matter how you master trading discipline, control your emotions, and manage risk, you cannot profit from a market unless your strategy has an edge. If you want, risk only 0.1% of your account per trade; without an edge, the account would slowly bleed out. The only way to know if your strategy has an edge in a market is by backtesting it.
Backtesting your strategy before trading boosts the likelihood of profitably trading in the market. Emotional control and risk management only help a profitable strategy make money; they can’t do anything if the strategy has no positive expectancy.
To improve a strategy:
Another reason to backtest a strategy is to see how to tweak it to improve performance. This involves altering some variables or adding new variables to see how they improve performance. The process is sometimes called optimization. Too much of it can lead to curve-fitting and diminish the strategy’s robustness.
Check for correlation in trading:
For the more advanced, you might want to backtest a portfolio of trading strategies. Even if you have 20 very good, backtested trading strategies, they might not work well together if they have many overlapping trades.
Who Should Backtest Strategies?
Every trader should consider backtesting. The reason is simple: there is only one way to determine whether a trading strategy is profitable, and that is by gathering statistics, data, and numbers to determine whether the strategy has made money before. If not, why should the strategy start making money now? If the strategy has been profitable, you might have something precious.
Related Reading
- How to Automate Trading
- Introduction to Algorithmic Trading
- Trading Risk Management
- How to Set Up Algorithmic Trading
Guide to Backtesting Trading Strategies
When you backtest a trading strategy, you simulate how the strategy would have performed on historical data. The process helps you determine if a trading strategy is worth pursuing before you trade it with real money. You perform a backtest by following a few simple steps:
Define the Logic of the Strategy
The first step is to define the strategy you want to backtest and clearly state the criteria for each action with specific trading rules. The logic you wish to backtest should be clear. For example, if your strategy is a moving average crossover system, you clearly state the following:
- Type of moving average
- Period of the two moving averages
- Entry and exit criteria
As an example, the entry and exit logic could be (as an example): When the 20-day SMA crosses above the 100-day SMA, exit any short position and enter a long one. When the 20-day SMA crosses below the 100-day SMA, exit any long position and enter a short position.
Select the Market(s)
At this stage, you specify the market you want to test the strategy on and the time frame. The market you backtest must be where you want to trade the strategy. You don’t use a strategy backtested on the silver market to trade the gold market, even though both are precious metals. Note that you can backtest the strategy on different markets and on different time frames and choose the ones with the best performance, but beware of curve fitting.
Get the Data
You will need historical data of the market you want to backtest. While some trading platforms offer some data, they may be limited. You may have to pay to get enough historical data for your backtesting. You have to divide your data into two:
- In-sample data for backtesting
- Out-of-sample data for optimization
Nevertheless, be careful about the data you backtest on. Your backtest is only as good as the quality of your data, and plenty of insufficient data exists.
Implement Your Backtesting
You may have to learn a programming language like Python, but some trading platforms, like TradeStation and Amibroker, use easy-to-use languages.
Evaluate Your Results
After the backtesting, you must evaluate your result to determine how the strategy was performed. Here are some of the performance metric statistics used in trading:
The Total Number of Trades
This tells you how many trades were taken during the period under study. The higher the number of trades, the more reliable the result.
Holding Time
This is the length of time that the strategy has open positions in the market. The lower the holding time, the better.
Profit Factor
This is the total profit divided by the total loss for the entire trading period. It indicates the amount of profit per unit of risk. A profit factor of more than 1 indicates a profitable strategy, but the higher, the better. Note that on some platforms, this is presented as the “Average size of winner/loser.”
Maximum Drawdown
This is the difference between the highest and lowest amount the trading capital reached during the backtesting. The lower the maximum drawdown, the better the strategy. The final part of a backtest is to backtest out-of-sample or incubate the strategy (more about this later in the article). This involves testing the data on unknown data, i.e., finding out how well the backtest predicts the future unknown data.
You can backtest a trading strategy on a platform or spreadsheet. Most trading platforms have a strategy tester section where you can backtest your strategy, but not all of them are free to use. Alternatively, you can use Excel or a spreadsheet, a free backtesting tool.
How to Backtest a Trading Strategy – Examples
Here is a simple example of how you backtest a trading strategy:
Suppose you have a swing trading strategy that assumes if the S&P 500 Index has a positive return in the past month, it will likely deliver a positive return over the following week. You can backtest this idea to see if it’s profitable.
Market: The S&P 500 Index. You can test this strategy on e-mini S&P 500 futures or the SPY ETF (SPY ETF trading).
Strategy Rules:
- Go long at the next day’s opening if the previous monthly bar closes with a positive return.
- Exit at the close of the trading week.
Since the signal is based on monthly data, you can access over 60 years of historical data to test it. After testing, evaluate the strategy’s performance using metrics such as:
- Profit factor
- Sharpe ratio
- Maximum drawdown
- Other performance indicators
If the strategy’s historical performance is strong, it could perform well in current markets. If the results are poor, you can discard the strategy or tweak and retest it.
Evaluating Backtest Performance
Since it is a monthly signal, you can get market data for more than 60 years. After testing, you evaluate the performance using stats like profit factor, Sharpe ratio, maximum drawdown, or any other statistic that measures the performance of a trading strategy. If the performance of historical data is good, you can assume that the strategy will perform well in the current market, but if the performance is poor, you may discard the strategy or tweak and retest it.
How to Backtest a Trading Strategy – Turnaround Tuesday Strategy
Let’s end this article section and make another specific backtest with trading rules and settings. The Turnaround Tuesday strategy is one of the most well-known yet still works well.
Here’s a set of trading rules for an overnight S&P 500 (SPY) strategy:
- Today is Monday.
- The closing price on Monday is at least 1% lower than Friday’s close.
- If both conditions are true, enter at Monday’s close.
- Exit at Tuesday’s close.
This strategy holds SPY for just 24 hours. When backtested, the equity curve showed promising results:
- Number of Trades: 171
- Average Gain per Trade: 0.67%
- Performance: Consistently strong even during bear markets
This backtest took less than two minutes using Amibroker, demonstrating the value of backtesting in validating or rejecting trading ideas.
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How to Backtest a Trading Strategy for Free?
You can backtest a trading strategy for free using software like Microsoft Excel (Libre Office), TradingView, NinjaTrader, Trade Brains, etc. As a retail trader, you may want a free platform for backtesting, and there is some free software available in the market for backtesting a trading strategy.
How to Backtest a Trading Strategy on Paid Platforms?
You can backtest trading a trading strategy on many paid platforms. The paid platforms may offer more features than the traditional free spreadsheet. One of the most commonly used paid platforms is Amibroker, a platform we have used for many years.
The analysis window of the Amibroker platform allows you to back-test your trading strategy on historical data. At about 450 USD for a lifetime license, the platform is cheap, and it offers full customization features for backtesting and some lightning-fast optimization features.
It is easy to test strategies on a portfolio level with the platform. Practically, all of the backtests done on this website are done by Amibroker or TradeStation. We are incredibly pleased and use it for live trading by adding just a little extra code. Considering the cheap and its features, Amibroker is a good option for most retail traders.
Based on our experiences, we have created an online Amibroker course that consists of several modules:
- Basic
- Live trading
- Trading strategies
How Long Back in History Should a Strategy Be Backtested?
A trading strategy should be backtested as far back in history as possible. Nevertheless, this depends on many factors. It is easy to say backtest for one year or two, but statistically, it is a question of duration and sample size. That said, we recommend including several types of markets, like bear and bull markets.
Market Conditions and Sample Size
Bull and bear markets behave differently. When backtesting, the key is to have enough sample size (the number of trades or observations) to represent the population (in this case, the market).
So, in terms of duration, you would need as much time as possible to get enough trades to have a statistically reliable result. While a sample of 250 trades may be sufficient, the bigger the sample size is, the smaller the margin of error (in most cases) and the more reliable the result.
Balancing Trade Sample Size and Backtesting Period
A 500-750 trades sample is suitable if your trading system generates enough trades. The best is to have both a large sample size and a long test period. The number of trades you get would depend on the strategy and how many trades it generates per day, week, or month. For a day trading strategy that produces a trade per day, it means you get about 250 trades per year, so backtesting over a 2-3 year duration should be able to give you enough data to make reliable assumptions.
Not so fast. The problem is that 2-3 years is short and usually only includes one business cycle. We like to backtest using about 20 years, so we are sure we have included bear markets in our dataset.
Trade Frequency and Backtesting Period
On the other hand, a swing trading strategy that generates only four trades per month would get you only about 48 trades per year. So, to get up to 500 trade samples that would ensure reliable assumption, you will have to backtest over more than 10 years. Thus, having fewer trades over a more extended period might be better than having many trades over a shorter period.
How Do You Backtest a Strategy in Excel or a Spreadsheet?
You backtest a trading strategy in Excel or a spreadsheet by getting data, loading it into Excel, making the trading rules, coding them, and then backtesting them.
Let’s explain:
Get Your Data Into Excel
There are two basic options to do this:
- Manually download historical data from Yahoo Finance directly as CSV and then load it into Excel. This works, but you’ll need to re-download that historical data and then copy and paste either the entire dataset or a subset to update your strategy.
- Use code to grab data from Yahoo Finance automatically. You can search on Google for some VBA codes you can use.
Some third-party tools, like AnalyzerXL, can make the job simple:
- Create Your Indicator: Now that you have your data in Excel, you can use them to construct an indicator or indicators for your strategy. You can use different columns to create different indicators or aspects of an indicator, each taking part in the calculation. You can use other add-ons or third-party tools for more functionality.
- Formulate Your Trading Rules: After creating your indicator, you must formulate your rules. You make the calculation in a new column called the “Signal” column. Your trading rule can be as simple as going long if the calculation is above a certain level and short if below it. But you could also have more complex rules with criteria for when to vary the position size or when to stay out of the market.
- Get the Equity Curve: You can get the equity curve with the Excel function, but if you need other trading stats, like CAGR and the Sharpe ratio, to compare the strategy with different strategies, you will have to do some work or use a third-party tool like AnalyzerXL.
We used Excel as our primary backtesting tool up to 2017. Excel is a lot better than most traders imagine! You don’t need any fancy tools to backtest; the principal asset is, after all, you, who put in the trading rules.
Using Excel for Backtesting and Live Trading
Excel can be a handy tool because you usually need to test a strategy on one instrument at a time. This way, you see small details you otherwise wouldn’t. We even used Excel for live trading. We had a VBA script that we used for both sending orders and closing our positions. It worked well but is less dynamic than dedicated software platforms (like Amibroker, Tradestation, etc.).
Do Professional Traders Backtest?
Yes, professional traders always backtest their strategies before deploying them. They know how vital backtesting is to a trading strategy’s profitability and cannot afford to make the mistake of trading a strategy that is not backtested. Some even forward-test their strategies—if they are intraday trading strategies that can generate enough trades within a short period—before committing real money to them.
Even professional traders who use discretionary trading methods still backtest their strategies manually, going back in time to check the occasions where their trade setups occurred and how the market reacted. All serious traders keep a trading journal.
Can You Backtest for Free?
Yes, you can backtest for free, for example, by using Excel or TradingView. Some trading platforms allow you to test your strategy for free, but you may have to look for historical data from market data vendors to get enough data. Some free backtesting platforms include:
- TradingView
- NinjaTrader
- So on
Alternatively, you can use free software like Microsoft Excel, which enables you to backtest using the Excel function. But you have to source your data elsewhere. A typical data source is Yahoo Finance, where you can download the data manually or write a code to do that. But, if using free data, please be careful about bad data.
How to Backtest a Strategy in Python?
To backtest a strategy in Python, you need to follow the steps below:
Import the necessary libraries for backtesting.
- Download the needed market data.
- Calculate daily returns.
- Create strategy-based data columns.
- Create strategy indicators.
- Create signals and positions.
- Implement the backtesting.
- Analyze results
Python is a free, open-source, cross-platform language with a rich library for almost every imaginable task, including automated trading. Thus, It is an excellent choice for backtesting a trading strategy. We have written about Python trading strategies in a previous article.
How Do You Backtest Strategies Without Coding?
To backtest a trading strategy without coding, you must use code-free software like Excel or spreadsheet. Such a platform allows you to create codes with a simple drag-and-drop interface. Examples of code-free platforms include:
- TradeStation
- Amibroker
MetaTrader 5 - TradingView
- QuantShare
- Forex Tester
What are the Most Common Trading Performances and Metrics?
The most common trading performance and metrics are the following:
- Sharpe Ratio
- Ulcer Index
- Treynor Ratio
- Max drawdown
- Sortino Ratio
- Risk-adjusted return
- Profit Factor
There are plenty to choose from! There are many ways to evaluate a backtest. Just looking at the result, the CAGR, or the annual returns, might need to be more accurate.
Let’s look at the most common metrics:
- Drawdown: Drawdown measures the decline in the value of a trading account from its peak to its lowest point. It helps traders understand the potential losses they might incur during market downturns.
- Sharpe Ratio: The Sharpe ratio is the most widely used performance metric in backtesting and trading. It measures the risk-adjusted return of a trading strategy by taking into account the volatility of returns. A higher Sharpe ratio indicates a better risk-adjusted return.
- Profit Factor: The profit factor measures the ratio of the total profit to the total loss generated by a trading strategy. A profit factor greater than 1 indicates a profitable strategy.
- Sortino Ratio: Similar to the Sharpe ratio, the Sortino ratio focuses on downside risk by considering only the standard deviation of negative returns, providing a more specific view of risk-adjusted returns.
- Ulcer Index: The Ulcer Index quantifies the depth and duration of drawdowns.
- Treynor Ratio: Similar to the Sharpe ratio, the Treynor ratio considers the risk-free rate in the denominator, making it helpful in assessing the risk-adjusted performance of portfolios with different risk profiles.
When you have completed a strategy backtest, you’d like to know if it’s good or bad.
Evaluating Strategy Robustness and Risk
How robust is the strategy? Is it likely a result of chance? This is what strategy performance and metrics are all about. If you’re a short-term trader, we are pretty confident that most traders would abandon a strategy if the drawdowns are too big, no matter how high the returns. This is why you need to look at strategy and system performance metrics.
What Is Survivorship Bias in Backtesting?
Survivorship bias in backtesting happens when you consider only the data of successful stocks or strategies, leading to overestimating performance. We can call it hindsight bias.
Survivorship bias in trading and backtesting is about the things we don’t see or, to a certain degree, ignore. We tend to see the winners and not the losers. Unfortunately, this is very typical in trading and backtesting. To avoid this, you need to understand survivorship bias in trading.
Let’s give you an example of survivorship bias in backtesting:
Suppose you want to backtest a momentum trading strategy among the S&P 500 stocks. You download all the tickers from today’s components and discover that the plan has worked well. But you are wrong! You have not included all the stocks that have been taken out from the index. Unfortunately, the ones taken out are likelier to be poor, not star performers. Thus, you are overestimating the results. Our backtests show that this is a significant problem.
The Importance of Good Datasets
Good datasets are the cornerstone of backtesting. What’s the point of backtesting deficient or erroneous data? You’ll make conclusions based on the wrong assumptions. Good data is essential in trading. After over twenty years of day trading, we have expensively experienced the importance of good data.
The Importance of Data Quality in Backtesting
The famous saying “garbage in, garbage out” is indeed true. Your backtest is only as good as the data you are testing on. Make sure you are backtesting on reliable and “clean” data. In the long run, spending money on a good data source for backtesting pays off. We have probably lost tens of thousands of dollars on trading strategies based on “garbage.” Sad but true.
What is Backtesting Bias?
Backtesting bias refers to potential flaws and errors in your backtest that might not represent actual results when you start trading your strategy live. It occurs for multiple reasons, the most obvious being curve fitting, slippage, commissions, curve fitting, survivorship bias, erroneous data, look-ahead bias, etc.
Most of these biases are covered in this article under separate headings. Unfortunately, backtesting has flaws, but it’s mainly based on the trading rules and the data put in. Backtesting frequently differs between simulated results and live trading. Backtesting is influenced by many factors, such as:
- Mathematics
- Statistics
- Psychology and more
This makes it susceptible to biases that can distort the outcomes heavily and render a backtest more or less useless. To avoid backtesting bias, traders must develop strategies and test them in good faith, avoiding bias as much as possible. They must be strict about testing with different data sets from those on which they train their models.
Let’s look at factors that lead to backtesting bias:
Optimization Bias
Similar to Murphy’s Law, optimization bias suggests that if something can go wrong, it will. This bias, also known as data snooping bias and curve fitting, arises when an algorithm is overloaded with numerous parameters, fine-tuned according to available data.
Such an approach tests the algorithm solely on past events and is unlikely to predict the future well. To avoid optimization bias, keep the simulation system as straightforward as possible, making it simple. Reduce the number of parameters. The fewer rules you have in the strategy, the more likely it is to hold up well in the future. Please read simple vs complex trading strategies.
After completing backtesting, it is recommended that the algorithm be subjected to new, unfamiliar data to validate its authenticity and effectiveness. This can be out-of-sample backtesting or walk-forward.
Look-Ahead Bias: The Perils of Foresight
Look-ahead bias is the temptation to use future or hypothetical information in a backtest when you can access the entire dataset. This is much easier than you imagine, and we have done this ourselves many times. It’s not only the trading rules, but it could also result from inadvertently setting the wrong settings in the software.
Avoiding Look-Ahead Bias in Backtesting
Conducting backtests on the same dataset increases the likelihood of unintentionally introducing a look-ahead bias into the system. To counter look-ahead bias, it’s imperative to ensure that live trading and backtesting employ the same algorithm or code. Doing so eliminates the risk of the program inadvertently peering into the future, thereby preventing look-ahead bias. We recommend using a demo account for months before you do live trading. This is a perfect filter for weeding out bad strategies.
Survivorship Bias: The Neglected Perspective
Coders and data scientists often ignore survivorship bias. Backtesting with a current stock database exclusively considers stocks that are currently active, omitting those that have been delisted. This phenomenon is aptly labeled survivorship bias and can massively distort a trading strategy.
Mitigating Survivorship Bias in Backtesting
Consider a strategy aimed at outperforming the S&P 500. If you backtest exclusively with stocks constituting the index, your results are likely tainted by survivorship bias. To mitigate this bias, consider databases that include delisted stocks or opt for more recent data when conducting your backtests. Norgate has databases that include delisted stocks.
Neglecting Market Impacts: The Pricing Oversight
Backtesting data history does not account for the actual execution of trades, potentially leading to an oversight of market impact. Since trading and pricing are interlinked, disregarding market impact can introduce bias into backtesting results. The most likely cause is slippage.
Impact of Liquidity on Backtesting
If you are trading illiquid stocks, slippage will likely distort a lot. On the other hand, if you are trading super liquid stocks like SPY or Apple, slippage is likely to be fine. How much is commission and slippage in live trading? The link provides data and statistics for our trading, and for SPY and QQQ the trading frictions are close to zero.
Accounting for Market Impact in Backtesting
Always assume that prices will move against you when you trade to rectify this oversight. This conservative approach eliminates bias, providing more accurate results and a clearer understanding of market impacts. As a rule, ALWAYS assume a backtest to perform worse in live trading.
Any Backtest Should Be Rigorously Tested and Met with Skepticism
Instead of viewing backtesting as a validation of the strategy, consider it a filtration process for eliminating flawed strategies. Every backtest is, in one way or another, somewhat curve-fitted based on the past. Adhering to this strict methodology can achieve more precise and unbiased trading strategies.
Does Backtesting Work?
Backtesting works because it is the best way for most people to approach trading and investing. Here are some arguments for why backtesting works:
Backtesting as a Tool for Hypothesis Testing
Backtesting lets you confirm or falsify a trading idea. It works because you can quickly check if something has worked in the past or not. Is, for example, the Turnaround Tuesday strategy in stocks true or just a myth? Just define the rules and start the backtest. You’ll find out in five minutes.
The Efficiency of Backtesting
Do you see an interesting pattern in the chart? Then, quantify it with strict buy and sell rules and test it. Did the strategy work in the past? If something has yet to work in the past, you can easily falsify your hypothesis and go on to test another idea. Because most ideas don’t work, you should not spend much time testing a hypothesis.
Many traders waste months, even years, in programming software and tweaking their strategies only to find out it was a waste of time. You don’t need “perfect” strategies to make money in the markets. You need many strategies that complement each other.
What is the Holy Grail in Trading?
Why build a portfolio of quantified strategies? We have written multiple times on this website that one of the main reasons for your success (or no) depends on your ability to test and generate trading ideas. One of our main trading lessons is we spend about 80% of our time testing ideas back and forth between ourselves.
Backtesting Exploits the Law of Large Numbers
Your computer can easily trade and supervise hundreds of strategies. This lets you exploit the law of large numbers and diversify into:
- Time frames
- Asset classes
- Directions
- Types of strategies
The main reason for the Medallion Fund’s success is twofold: They use enormous amounts of data to generate hundreds of uncorrelated strategies. Because of the low internal correlation among the strategies, they can use leverage to boost returns.
Low correlation among your trading strategies is one of the most important factors in trading. Why? Because if you lose money in one strategy, you might gain in another uncorrelated strategy. We have covered this topic in many articles:
- What does correlation mean in trading? (Trading strategies and correlations)
- Uncorrelated assets and strategies – benefits and advantages (examples and backtests)
- Does your trading strategy complement your portfolio of strategies?
Backtesting Reduces or Removes Emotions
Evidence indicates that individual investors underperform the averages, and women are better than men. The main reason for this is behavioral mistakes.
Overcoming Trading Biases
Common trading biases include:
- Selling during panic-induced downturns.
- Buying after a significant price rise.
In most cases, you need to do the complete opposite—buy during downturns and sell after sharp gains.
A backtest can’t capture such mistakes, so you need to stick to the trading plan. To stick to the trading plan, trade smaller than you’d like or prefer. This is the best way to keep detachment from money. Likewise, women do better because they:
- Save
- Invest
- Forget about it
The Perils of Overriding Your System
They aren’t trying to be geniuses! They don’t have any ego. The closer you follow the markets, the more likely you are to overrule your systems when your “intuition” tells you to sell or buy. But most of the time, the intuition is plain wrong, unfortunately. Overruling your systems and strategies is unlikely to work. You haven’t backtested overruling, so how do you know if it works? That’s why you backtest trading strategy.
Backtesting Saves You Time
We tested about 15 hypotheses in Silvers Miners (ticker code SIL) in about 1.5 hours. One of those ideas seems promising; the rest are a waste of time. You can generate and test hundreds of strategies in just a single day. Even better, you can falsify or confirm ideas quickly. Trading is mainly about trial and error. And luckily, backtesting a trading strategy is a great tool for that and at the same time, it saves you a lot of time.
Disadvantages of Backtesting
Of course, backtesting has drawbacks, disadvantages, and negatives. You rarely find trading strategies that perform better in live trading than in tests. Why is it so? This is because of the disadvantages of backtesting. You need experience in testing to avoid the many pitfalls along the way.
Let’s look at the disadvantages of backtesting. There are many reasons why backtesting doesn’t work, like:
- Curve fitting
- Market cycles
- Chance
- Luck
- Randomness
The good thing is that you can avoid or at least minimize many of testing’s disadvantages. We are proponents of quantifying trading strategies, but you need to understand how to avoid the many pitfalls in testing.
Live Trading Will Never Be as Good as the Backtest
As a rule of thumb, you can expect your trading strategy to perform worse in live trading than in the backtest. Actual results will never be as good as theoretical backtesting. It’s almost 100% certain that the real-time results will be worse than the actual backtesting.
The Backtest Might Be Liable to Favorable Conditions
The backtest is performed over a certain period, and the markets may have been favorable to that trading strategy during that period. Most traders neglect this aspect. Testing over a longer time frame might minimize this. Take, for example, the so-called trend-following strategy. They perform badly in specific periods covering many years and much better in others.
If you are testing a moving average breakout, it might yield mediocre results over five years. But this is a typical strategy that needs to be tested over at least 10 years. You don’t want to change a plan in response to one year just because something didn’t work. That’s when you’re almost guaranteed it would have worked the following year had you kept it as it was.
The ever-changing market cycles make trading difficult. You have to accept drawdowns to make money; every strategy has drawdowns.
Backtests Are After the Fact – Hindsight Bias
You can only trade the strategy after the fact. For example, you are using entry at the close. The problem is, you only know if it’s a trade after the close. To trade on the close, you might have to guess/estimate there will be a trade at the close. So, when backtesting, it’s crucial to consider this factor. One way to do this is to trade at the open the day after the signal.
Backtesting Involves Elements of Curve Fitting
Another reason is the curve-fitting aspect. This certainly applies if you have a lot of parameters or variables. It’s easy to create a system that has performed remarkably well. You just need to input a lot of parameters. That will explain the past, but most likely not the future. The simpler the system, the more likely it’s to stand the test of time.
In The Way of the Turtle, Curtis Faith explains some incredibly simple trend-following strategies. Over several decades, they have worked well in currencies and commodities (not on stocks). Nevertheless, over 1-3 years, they sometimes experience quite huge drawdowns. Still, these systems are so simple that they are less prone to being curve-fitted.
Backtesting Involves Survivorship Bias
Another problem in backtesting is survivorship bias. This relates to using stocks/tickers that have “survived” the testing period. For example, in 2008/2009, many stocks went bust (Lehman being an example). This means that companies that have gone bankrupt are excluded from the analysis at later dates. This might be less of an issue in day trading, but not when testing over a much longer time frame.
Downloading quotes for REITs back to 2005 will exclude several stocks that went bust during the financial crisis. Also, if you backtest indices, you will unlikely be a victim of survivorship bias.
Chance, Luck, and Randomness Overestimate Backtesting Results
If you test many strategies, some will show good returns simply by chance. Unless there is a logical reason for a plan, you are guaranteed to find many good strategies the more you test. Hence, we recommend that there be some reasoning behind the parameters.
Backtesting Involves Garbage In, Garbage Out
The quotes and data you buy or download are usually not 100% correct. If you use high and low in my research, you can be sure there are errors compared to live trading. There are a lot of wrong quotes on high and low! This will have a significant impact in actual trading, most of all the factors mentioned in this article.
Transaction Costs in Backtesting Are Unknown
Slippage and commissions are easy to underestimate. Commissions are easy to calculate, but slippage is not. If you are a serious trader, keep records so I can calculate the total costs for commissions and slippage. This is a vast unknown, mainly if you base your strategies on chasing the stock. If you wait to get hit, this is, of course, not an issue.
Markets Change – No Backtest Can Accommodate Change
The market changes all the time. The future is unpredictable, and you can bet there will be random and dramatic changes in the marketplace. No one expected terrorists to hijack planes and send them into a skyscraper. No backtest can capture black swan events. Such unpredictable disasters will happen sooner or later.
Correlations among different asset classes also increase during such happenings. You can never backtest such things.
Backtesting Ignores Trading Biases and Behavioral Mistakes
You need to understand the most prominent trading biases. The psychological aspect is just as important as the strategy. Can you handle drawdowns and continue trading? Can you follow the strategy? Based on my experience, you must consider this thoroughly before implementing a strategy. It’s a lot easier said than done to follow a strategy 100%.
Backtesting Should Always Prepare for the Worst
As a rule of thumb, it might be wise to expect a maximum of 50% of the profits from testing. You can exaggerate slippage and commissions and expect a much higher drawdown than in the backtest. Hope for the best, but prepare for the worst. Never be too optimistic when seeing a very nice equity curve; the downfall will be bigger—only real trading matters.
Related Reading
- Best Automated Trading Platform
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- High-Frequency Trading Algorithm
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- Algorithmic Trading Strategies
- Futures Trading Algorithms
11 Best Backtesting Software for Traders
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- Platform Compatibility: Supports leading trading platforms like:
- NinjaTrader
- TradeStation
- MetaTrader (MT4/MT5)
- Interactive Brokers
- Sierra Chart
- Quant Tower
- Unmatched Reliability: 100% uptime guarantee ensures uninterrupted trading operations.
- 24/7 Support: Round-the-clock assistance to keep your automated strategies running smoothly.
QuantVPS delivers a speed-optimized, robust environment for executing automated trading strategies efficiently and continuously.
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2. TrendSpider: A Smarter Way to Backtest Trading Strategies
TrendSpider has fully automated AI-driven trendlines, Fibonacci, and multi-timeframe analysis for stocks, Forex, crypto, and futures. Add a robust backtesting engine with an excellent technical analysis platform.
Pros
- Deep backtesting strategy
- Reporting & analysis
- Automated trendline, pattern & candle detection backtesting
- Powerful point-and-click multilayered backtesting
- Launch bots from backtests
- Multi-TimeFrame & Fibonacci analysis
- Real-time exchange
- Data included in price
- Stocks, ETFs, Forex, Crypto, Indices & Futures
- Newly integrated AI assistant built-In
- Create custom indicators.
- Exceptional user support: Free 1-to-1 training
Cons
No Social Backtesting TrendSpider is built from the ground up to detect automatically:
- Trendlines
- Chart patterns
- Candlesticks
- Fibonacci patterns
This means backtesting has already been built into the heart of the code. Its most recent features are:
- Scanning for and backtesting news events
- Analyst estimates
- Financial data
- Splits
- Dividends
- Earnings
All of this is possible without needing to code. TrendSpider has implemented a strategy tester that allows you to type what you want to test freely, and it will do the coding for you. This is a smooth, simple, and incredibly user-friendly implementation. You can also adjust your backtest conditions on the fly, and the “Price Behaviour Explorer” and “System Performance Chart” automatically update with trade statistics like:
- Win rate
- Profitability
- Drawdown
3. Trade Ideas: AI-Powered Backtesting and Trading
Trade Ideas provides the best AI-driven automated backtesting and auto-trade software, offering day traders specific audited trading signals for high-probability trades. Trade Ideas introduces us to a world where you do not need to manually backtest your stock trading theories for hundreds of hours to get an edge in the market. With Holly AI, the work is done for you.
Pros
- 3 High-performing AI trading algorithms
- Fully automated backtesting
- Exceptional stock scanning capabilities
- Specific audited trade signals
- Auto-trading with AI signals
- Live daily trading room
Cons
- No mobile app
- Black box backtesting (unable to develop detailed strategies)
Trade Ideas has three AI algorithms that automatically backtest stock chart patterns and volume conditions to find high-probability trades for day traders. Trading algorithms that constantly backtest millions of real-time conditions to find trading opportunities include:
- Holly
- Holly 2.0
- Holly Neo
Each recommended trade has a win probability and a full set of backtested data for you to review.
AI Virtual Trading Analyst Holly
- Three different constantly evolving AI algorithms
- Chart-based AI Trade assistance
- Entry and exit signals
Risk Assessment
- Detailed information on the backtested performance of the recommended trades.
Build and Backtest Any Trade Idea
- Easy-to-use, point-and-click backtesting system
Autotrade w/ Brokerage Plus and AI
- Advanced auto trading commission-free with E*TRADE.
Backtesting
- AI algorithms developed by Trade Ideas are why you want to sign up.
I had a lengthy Zoom session with Sean Mclaughlin, senior strategist at Trade Ideas, to learn how the algorithms work, and I was very impressed. This company is laser-focused on providing traders with the best data-supported trading opportunities.
Currently, four AI systems are in operation, with Holly AI being the original and most prominent. Here’s a breakdown of the systems:
Holly AI
- The Original Algorithm: Holly applies over 70 black box strategies across US and Canadian stock exchanges, including pink sheets and OTC markets.
- High-Volume Backtesting: 10,000+ stocks means millions of backtests every day.
- Winning Strategies: Only strategies with a backtested win rate over 60% and an estimated risk-reward ratio of 2:1 are selected for potential trades the next day.
Holly Grail’s Trade History
- Strategy & Exits: Holly Grail’s trade history shows backtested strategies and their outcomes, demonstrating the system’s performance in real-market scenarios.
Trade Ideas operates three key trading styles with each AI engine:
- Conservative
- Moderate
- Aggressive
Trade Ideas AI Systems
Holly 2.0
- Aggressive Strategy: Holly 2.0 is designed for higher risk but with the potential for higher rewards. It focuses on more aggressive day trading scenarios.
Holly Neo
- Real-Time Chart Pattern Trading: Holly NEO is a newer AI system that uses four distinct strategies to identify trading opportunities in real-time:
- Pullback Long: Seeks stocks with a recent price drop poised to move upward on higher volume.
- Breakout Long: Identifies stocks breaking through key resistance or making new highs.
- Pullback Short: Finds shorting opportunities during a temporary pullback in price.
- Breakdown Short: Discovers shorting opportunities when upward momentum fails, and price starts to break down.
I have highlighted a trade Holly AI (Holly Grail) recommended in the chart below. The chart for Cleveland Biolabs (Ticker: CBLI) made a 25% profit within 4 hours, which differs from how the buy and sell signals are depicted on the chart.
4. TradingView: Free Backtesting Software for Stocks, Forex & Cryptos
TradingView provides excellent free backtesting software for:
- Stocks
- Forex
- Cryptocurrencies
You can also auto-trade using webhooks to third parties and their integrated brokers. TradingView is the ultimate all-rounder, with global screening and charting for all stock exchanges, plus a community of 13 million active users sharing ideas, strategies, and indicators.
There is no doubt about it; I love TradingView and use it daily. I regularly post charts, ideas, and analyses and chat with other traders. The TradingView community is focused on trading and investing, and the service is first-class.
Pros
- Social Features: Social-first platform with chat, publishing, and follow functionalities.
- Backtesting & Development:
- Easy backtesting and development using Pine Script.
- Includes a strategy tester and manual backtesting options.
- Global Data Coverage: Access to backtesting data for cryptocurrencies, Forex, and stocks in the USA and globally.
- Large Community: Over 20 million active users.
Cons
Limited Features
- No real-time news integration.
- Pine Script coding skills are required for advanced features.
- Backtesting is limited to single instruments, not entire markets.
TradingView is the best free backtesting software available. Features include:
- Backtest stocks, cryptocurrencies, and Forex on the free plan.
- Pine Script engine enables powerful and flexible chart backtesting.
- Access up to 100 years of market data.
TradingView has an active community of people developing and sharing stock analysis systems. With the premium-level service, you can create and sell your own. The community also offers many indicators and systems for free.
5. Stock Rover: Backtesting Software for Fundamental Investors
Stock Rover is my favorite backtesting software for value, growth, and dividend investors.
Pros
- Stock Rover Review: Winner of Best Stock Research Tool for Income, Growth, & Value Investors.
- 650+ Financial Screening Metrics
- Potent Stock Scoring Systems
- Unique 10-Year Historical Data for Backtesting
- Warren Buffett Value Screeners & Portfolios
- Real-Time Research Reports
Cons
- No Social Community
- Not Recommended for Traders
Backtesting with Stock Rover
- Stock Rover provides 10 years of backdated financial data and scanning capabilities, surpassing most stock screening tools.
- Unique feature: Backtest screening results to assess historical performance of stock selection criteria.
- What makes it unique?
- A clean 10-year historical database of hundreds of vital ratios, calculations, and metrics.
- Allows users to “travel back in time” and test the effectiveness of their criteria in the past market.
6. MetaStock: Independent Stock Backtesting & Forecasting Software
MetaStock is one of the best independent, broker-agnostic stock backtesting and forecasting software platforms. It enables over 300 chart indicator backtesting strategies.
Pros
- Develop sophisticated systems and backtest entire markets with one click.
- Comprehensive profit, drawdown, and ROI reporting for practical analysis.
- Exceptional and intuitive forecasting software.
- Rank #1 for charts, indicators, and real-time news.
- Excellent customer support and educational seminars.
- Covers all global markets and is broker-independent.
Cons
- Requires scripting skills.
- No automated trade execution.
- No broker integration.
Backtesting MetaStock enables backtesting over 300 chart, price, and volume indicators, enabling the development of a highly granular trading strategy for stocks, Forex, and commodities.
As you launch MetaStock, you are presented with the Power Console, enabling you to quickly select what you want to do. Select System Test, and you will have access to 58 systems you can backtest.
Rapid Backtesting with Equis MACD Expert System
In the example below, I selected the Equis MACD Expert System and ran it on the entire Nasdaq 100. After 60 seconds, the backtest was completed, and I was presented with a list of every buy or sell trade and the drawdown on the portfolio chart that you can see above. You can click through any trade to learn its:
- Background
- Size
- Duration
- Profit or loss
Using Built-in Systems and Expert Advisors
MetaStock harnesses many built-in systems and expert advisors to help you understand and profit from technical analysis patterns and well-researched systems as a beginner or intermediate trader. This is a crucial area of advantage. Of course, the inbuilt systems will not make you rich, so you will want to backtest and develop your profitable system.
You can build a unique, backtested strategy with MetaStock using scripting or programming skills. If you need to gain the required skills, you can ask MetaStock or one of a considerable number of MetaStock Partners to assist you in creating your system.
7. Tickeron: Backtesting with AI Chart Pattern Recognition
Tickeron’s backtesting is automated, and its impressive AI-powered chart pattern recognition and prediction algorithms for stocks, ETFs, Forex, and Cryptocurrencies are impressive. Tickeron excels at providing:
- Thematic model portfolios
- Specific pattern-based trading signals
- Success probability
- AI confidence levels
Tickeron’s trading platform is unique and innovative. It combines artificial intelligence and human intelligence based on the community of traders, so you can compare what humans think versus what machines think.
Tickeron Review: Winner Best Day Trading Software
Pros
- Access to 45 Streams of trade ideas.
- Real-time pattern recognition for 40 markets, including stocks, ETFs, Forex, and cryptocurrencies.
- AI-powered trend prediction engines for smarter decision-making.
- Build customized portfolios with AI assistance.
Cons
- Limited custom charting options.
- Cannot plot indicators directly.
Tickeron uses AI rules to generate trading ideas based on pattern recognition. Firstly, they use a database of technical analysis patterns to search the stock market for stocks that match those price patterns using their pattern search engine. Each detected pattern has a backtested track record of success, and this pattern’s success is factored into the prediction using their:
- Trend prediction engine
- Pattern recognition
- Prediction and backtesting
Real-Time Pattern Recognition with AI
At the heart of Tickeron is its AI algorithms’ ability to spot 40 different stock chart patterns in real time. You can select which pattern you want to trade, and it will filter stocks, Forex, or cryptocurrencies currently showing it. Patterns are split into bullish patterns for long trades or bearish patterns for those who wish to go short.
Tickeron’s real-time pattern recognition benefits swing or day traders, where market timing is the top priority. Tickeron can also scan the entire market and suggest which patterns work best on a particular day. In the screenshot above, you can see “Today’s Top Ranked Patterns,” which rates the potential success of the patterns based on the market’s current trading activity.
Pattern recognition saves pattern traders a lot of work hunting for potential trade setups because it does all the work for them.
8. Portfolio123: Backtesting Software for Fundamental Investors
Testing of Portfolio123 shows stock screening and powerful backtesting software with a robust financial database and integrated commission-free trading with Tradier. Portfolio123 can be used by income, value, and growth investors but is also advantageous for swing traders.
Portfolio123 covers stocks, fixed income, and ETFs on US and Canadian exchanges, which is unsuitable for international stock investors. Nevertheless, you can design a fully automated real-time trading strategy with a broker to hold the stocks that pass your screen and sell those that don’t.
Portfolio123 Review: Winner Best Screening, Backtesting & Trading
Pros
- Over 470 screening metrics for detailed analysis.
- 10-year backtesting engine with robust performance insights.
- Unique access to 10 years of historical data.
- Pre-built model screeners for quick filtering.
- 260 financial ratios for comprehensive evaluation.
- Integrated $0 trading for added convenience.
Cons
- No integrated news feed.
- Lacks a mobile app for Android or iPhone.
- Initially complex to use due to advanced features.
- Technical analysis charting requires improvement.
Key Features
- Advanced Screening: Filter through 10,000+ stocks and 44,000 ETFs to efficiently match your specific investment or trading criteria.
Portfolio123 also has ranked screening, which enables you to rank the stocks that best match your criteria, filtering a list from hundreds of stocks to a handful. You can also define your custom universes, setting the macro criteria for which stocks are included in the sample. Over 225 data points will cover most ideas based on fundamentals.
Portfolio123 has 460 criteria, including:
- Analyst revisions
- Estimates
- Technical data
Backtesting Strategies with Portfolio123
You can also use Portfolio123 to screen stocks on their performance relative to the S&P500 or any other benchmark. You could develop a strategy to select stocks based on their historical performance versus the market. Building your Portfolio123 screener is theoretically easy; select Research -> Screens, and you can start to play. No programming skills are required to construct a Portfolio123 screener, but basic coding will undoubtedly help.
Leveraging Portfolio123’s Powerful Backtesting Engine
To create more powerful screening rules, you must study the coding logic and understand the names of the proprietary criteria. Backtesting Portfolio123’s backtesting engine is where the software shines. Expertly implemented, fast, and highly configurable, Portfolio123 has the best backtesting service for people serious about testing fundamental strategies.
Customizing Backtests with Granular Control
Portfolio123 enables you to be very granular in setting up your backtest with:
- Entry rules
- Slippage
- Weighing
- Rebalance frequency
- Custom timeframes
The Portfolio123 screener is built to make users test not just pre-built concepts but all sorts of hypotheses. You can use your universe, rank with your multi-factor rank, and run rolling backtests.
9. Interactive Brokers: Best for Fundamental Backtesting
IB is ideal for active investors and day traders seeking low trading costs and direct global market access. It offers backtesting and auto-trading through third-party software utilizing Signal Stack.
Pros
- Best fundamental backtesting in the industry
- Great trading platform
- Direct market access
- All markets and vehicles
Cons
- Must be an IB client
- Limited backtesting on chart indicators & supply/demand
A Comprehensive Trading Platform
Interactive Brokers provides direct market access for fast execution and best-in-class margin costs. They are the grandfather of online discount brokers. Not only are they a long-established company, but they are also large. It has a complete set of services, enabling you to trade practically anything on any market:
- Stocks
- Options
- ETFs
- Mutual Funds
- Bonds
- Foreign Exchange
- Futures and Commodities
Usually, when a company is well established, it loses its competitive edge. This is different with Interactive Brokers, which has a unique trading platform based on Trader Workstation (TWS). It is free to download and use as a client and is the single place to trade any of IB’s assets. Many advanced add-on tools, such as ChartTrader, which allows trading directly from charts, also plug into TWS.
- Continuous Futures: For commodity futures scanning and analysis.
- DepthTrader: For in-depth analysis of market liquidity.
- OptionTrader: Deeper Options Analysis with specific Options strategies.
- ProbabilityLab: To test the Probability Distribution of a particular trade.
- Portfolio Manager: For backtesting
Advanced Trading Tools for Diverse Strategies
There are 27 different advanced trading tools in total to suit every possible approach to the market. The “Portfolio Manager” tool within the powerful Trader Workstation (TWS) platform is well-designed and easy to use. It is designed to help portfolio managers balance and manage a portfolio of stocks.
Most portfolio managers do not buy and sell shares based on technical indicators like MACD, RSI, or Moving Averages; they buy and sell based on the fundamentals of a particular company. This is reflected in the unique parameters that are available.
10. TradeStation: Powerful Backtesting for Active Traders
TradeStation is a leading brokerage house with excellent execution and reasonable commissions. Did you know they have great backtesting software? TradeStation offers enough software and broker integration to compete with the other vendors.
Pros
- Powerful charting tools
- Good Algo and power tools
- Free software for brokerage clients
- Broker integration
Cons
- $1 per trade
- No market commentary or a chat community
Educational Resources and Strategy Marketplace
TradeStation offers TradeStation University a huge wealth of online videos to help you master their trading platform. TradeStation has also cultivated a systems and strategies marketplace called the “Strategy Network,” where you can purchase stock market systems from an ecosystem of vendors or even contract someone to develop your system in the “Easy Lacharte” code.
Intuitive Backtesting and Strategy Development
Backtesting TradeStation’s unique proposition lets you create powerful technical backtesting scenarios directly from the charts. There is no need for programming or script development; it is straightforward. Select your chart, timeframe, and indicators, then plug in the parameters you want for the buy and sell orders. Long and short trades are all covered.
Seamless Transition from Backtesting to Live Trading
The beauty is that you can turn the hypothetical system into an automated trading system with algorithmic trading applications because this is a broker-integrated solution. It is called TradeStation because it is where you can build a technical chart-based system and execute it automatically.
11. QuantShare: Backtesting Software for Quants
Quantshare is recommended for Quantitative Analysts who develop powerful automated systems and value a wide selection of shared user-generated ideas. Still, you need to be able to code.
Pros
- For Quants wanting to automate backtesting
- Super cheap
- Active community
- Very good backtesting
Cons
- Programming knowledge required
- Poor interface
- Challenging to use
QuantShare was new to me, and I was surprised by its feature set.
A Platform for Quantitative Traders
Do you backtest, forecast, and program algorithms to gain an edge in the market? Are you a hardcore programmer and mathematician? Then QuantShare is for you. Stock Systems and
Backtesting QuantShare specializes, as the name suggests, in allowing Quantitative Analysts to Share stock systems.
They have a huge systems marketplace with a lot of accessible content that you can test and use. If you have a programmatic mind, you can implement and test endless possibilities.
They also have powerful prediction models using neural networks. This is an advanced software for those with programming skills. QuantShare is difficult to use, and the interface requires serious development effort. The learning curve will take time on your part.
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