Stock backtesting is a tool that allows you to test a trading strategy without risk. The best stock backtesting platforms help traders assess how their strategies would have performed at various times in the past.
These are some of the top options for adding backtesting to your arsenal of trading tools.
| Backtesting Tool | Price | Coding Required | Key Characteristics | Best For |
|---|---|---|---|---|
| TrendSpider | $49.76/month | no | Advanced AI-powered algorithms, intuitive backtesting tools, including social data | Ideal for investors focusing on momentum and growth stocks |
| TradingView | Free | yes | Popular platform, easy-to-use backtesting, covers global stocks | Suitable for users comfortable with coding and wanting a large community |
| Trade Ideas | $178/month | no | Advanced AI-powered algorithms, intuitive backtesting tools, includes social data | Good for those wanting AI insights and intuitive use |
| FinViz | $24.96/month | no | Primarily a stock scanner, basic backtesting tools, 100 criteria for filtering stocks | Best for traders using stock screening with a focus on price action |
| Backtest Zone | Free | no | Easy to use interface that requires absolutely no tech skills and supports more than 70 exchanges. | The top choice for those who don’t mind sacrificing complexity in exchange for ease-of-use |
| Backtrader | Free | yes | Contains the bt-run.py script which automates most tasks, including choosing which data should be loaded, the format, etc | Anyone with a strong python background who wants an open-source backtesting tool |
| QuantConnect | Free | yes | Emphasis on quantitative and algorithmic trading, open source, includes fundamental and price data | Perfect for quantitative and algorithmic traders |
Best Backtesting Tools and Software
Some more general analytical platforms, like TD Ameritrade and NinjaTrader include backtesting tools, but here we’ll focus on tools primarily designed for backtesting.
Some of the backtesting platforms we like include:
1. TrendSpider
TrendSpider is an advanced technical analysis platform that’s distinguished by its integration of artificial intelligence and advanced strategy development tools.
The platform offers several distinct advantages. Most notably, TrendSpider now provides multiple paths for creating trading strategies: traditional manual configuration, natural language inputs, and a new AI Strategy Lab that can generate machine learning-based trading models. You can test these strategies against up to 50 years of historical data without writing code – though JavaScript-based custom indicators are still supported for those who prefer programming.
While TrendSpider emphasizes technical analysis and algorithmic trading, it has evolved beyond simple price action to incorporate AI-driven pattern recognition and custom data feeds. This has made it particularly valuable for systematic traders focusing on quantitative strategies across various timeframes and asset classes. For these reasons, it has also built up a loyal community of users.
💡 You can read more in our in-depth TrendSpider review.
The platform offers three subscription tiers: Standard ($49.76 per month), Enhanced ($58.12 per month), and Advanced ($82.70 per month) when billed annually, with each tier providing progressively more sophisticated features like variance testing and one-on-one training. Note that the Standard plan limits you to long-term backtesting, while the Enhanced and Advanced plans include both short-term and long-term backtesting, as well as access to the AI Strategy Lab.
2. TradingView
TradingView is another popular technical analysis platform with a large community of traders and investors who share their ideas and strategies on the platform. It’s cloud-based and includes all of the technical analysis features you would expect to find on a TA platform. One of the major advantages of TradingView is that most global stocks and other assets are covered.
TradingView’s backtesting comes in two varieties, which they refer to as “regular strategy tester backtesting” and “deep backtesting.” The key difference is that regular backtesting uses only the price data loaded on the current chart you’re using, which is limited by your TradingView subscription level. In contrast, deep backtesting uses price data going back further in time, starting from the beginning of the date range you specify.
Creating strategies on TradingView requires coding in the native programming language, Pine Script. This may sound complicated, but it’s actually quite easy to copy and adapt strategies shared by other traders.
TradingView offers three paid plans for personal use. These cost $12.95, $24.95, and $49.95 per month (billed annually). Month-to-month billing is also an option, but costs more. The backtesting tool is available on all three plans, and you can try any of them for free for 30 days. After the trial period, if you decide that you don’t want to pay, your account will be converted to a free plan with limited functionality.
💡 You can learn more in our extensive TradingView review.
Compare our top picks head to head: TradingView vs TrendSpider
3. Trade Ideas
Trade ideas is an advanced market intelligence platform that leans heavily on artificial intelligence. The platform includes numerous AI-powered algorithms that generate trade ideas and can be incorporated into a strategy. Trade Ideas also includes social data that is built into the algorithms.
The backtesting tools module is very intuitive and does not require coding knowledge. It also provides a very informative analysis of results and suggestions for optimizing a strategy.
Trade Ideas has three subscription tiers – free, $89 per month, and $178 per month (billed annually). Month-to-month pricing is also available but at a higher rate. The one big downside here is that the backtesting tools are only available on the highest tier.
4. FinViz
FinViz is primarily a stock scanner to filter stocks using a combination of descriptive, technical, and fundamental criteria. It’s also packed with other useful tools – including a backtester – so you can stay on top of your trading game.
The FinViz backtesting tool comes with 100 technical indicators and includes 24 years of historical data. It offers a good balance between being straightforward to use while still being very informative.
FinViz has a free plan, but it’s extremely limited and does not let you use the backtesting module. To unlock it, you’ll need to upgrade to the FinViz Elite plan, which is available for $24.96 per month on an annual contract. If you prefer to pay by the month, then it’s $39.50. You can learn more in our extensive FinViz review.
5. Backtest Zone
Backtest Zone is a free, web-based platform designed to make strategy testing accessible for traders of all backgrounds. Its user-friendly interface enables anyone to backtest a strategy without coding or advanced technical skills. It’s not an exaggeration to say that this is the easiest to use tool on the list.
This platform gives you access to historical data for over 70 exchanges, enabling you to test your strategies on a timeframe stretching from a stock’s IPO date to the current trading day.
Constructing a strategy is simple and straightforward. You’ll start by selecting your asset, then choose from a comprehensive library of popular technical indicators (e.g., Bollinger Bands, Parabolic SAR, Aroon Down, RSI, SMA, DEMA, etc) to define your entry and exit rules. When you finish making your selections, you can run your backtest with a single click.
While more advanced backtesting platforms may offer greater flexibility and capabilities, they often come with steeper learning curves that can be daunting for beginners. Backtest Zone prioritizes accessibility, giving you a powerful yet intuitive tool to start exploring systematic trading.
6. Backtrader
Like Backtest Zone above, Backtrader is also free, but it’s on the complete opposite end of the learning curve. While Backtest Zone caters to beginners with its simple, no-code interface, Backtrader is a powerful Python framework designed for users comfortable with coding their strategies.
The platform revolves around two key components: Strategies, where you define your trading rules, and Cerebro, the “brain” that manages the backtesting process. Backtrader provides a base Strategy class that you extend to define your own custom strategies. This involves overriding methods to specify your startup logic, your trading logic for each new data point, and any cleanup logic.
Once your strategy is defined, you create a Cerebro instance, load your historical data, inject your strategy, and let it run. Cerebro handles the complexities of backtesting behind the scenes, allowing you to focus on the high-level trading logic.
In summary, Backtrader is a powerful, feature-rich Python framework for backtesting trading strategies. While it requires coding skills, it offers a level of control and customization that sets it apart. If you’re comfortable with Python and want a tool that can grow with your needs, Backtrader is an excellent choice.
7. QuantConnect
At its core, QuantConnect distinguishes itself by providing a unified cloud-based infrastructure that seamlessly integrates research, backtesting, and live trading capabilities. The platform processes an impressive volume of over 500,000 backtests monthly and facilitates more than $45 billion in trading volume, serving a vibrant community of 346,000 quantitative traders and researchers.
What makes QuantConnect particularly powerful is its comprehensive asset coverage, which includes stocks. You can access data at various resolutions, from tick-level data to daily bars, allowing for strategies across different timeframes.
The development environment is notably flexible. It offers a web-based IDE, but it also supports local development through its LEAN CLI tool, allowing you to code in your preferred environment using Python or C#. This is complemented by VSCode integration and debugging capabilities. The platform’s backtesting engine, LEAN, is open-source and has been refined by contributions from over 180 engineers, ensuring its reliability and performance.
QuantConnect has numerous plans, including a free option that is very generous compared to some of the other freemium backtesters on the list. The cheapest paid plan is $8 per month, on an annual contract. However, you aren’t restricted from running backtests on the free plan so upgrading is purely optional.
What Is Stock Backtesting?
The term “backtesting” describes any process designed to evaluate how a strategy would have performed in the past. Stock backtesting uses historical data and technology to evaluate how a strategy would have performed if you had adopted it at some previous point.
The assumption is that strategies that were effective in the past will be effective in the future and vice versa.
Pros and Cons of Backtesting
Backtesting a trading strategy can tell you a lot about how it has performed in the past and may perform in the future. While we can be fairly certain the future will be different, knowing a strategy’s strengths and weaknesses can be invaluable. If a strategy didn’t perform well in the past, it’s unlikely to perform well in the future. However, it’s worth keeping the benefits and drawbacks of backtesting in mind.
✔️ Pros of Backtesting:
- Backtesting tools show you whether a strategy was profitable in the past.
- You will know what sort of drawdowns were experienced.
- Using backtesting tools is a very efficient (time-saving) way to test a strategy with hundreds or thousands of trades.
- You may be able to see what market conditions led to underperformance, and you may be able to apply filters to avoid trading when those conditions arise.
- You can optimize parameters to improve risk-adjusted performance.
- You can remove indicators or rules that don’t add value.
❌ Cons of Backtesting:
- In practice, real-world results are almost always worse than the results of backtests.
- There is a temptation to over-optimize a strategy so that it performs very well over a specific period of historical data. This is known as curve fitting or overfitting.
- A trading strategy will still need to be tested live to determine slippage and trading costs.
- You may become overconfident based on the results of an initial backtest. This often results in rushing to begin live trading without exhaustive testing on a stock market simulator.
- There is an inherent bias toward creating strategies using patterns that you know have worked in the past.
- In short time frames, like day trading, execution plays an enormous role in performance. Backtesting can still be a starting point for day traders, but paper trading is more important.
Effective backtesting has some basic requirements:
- The strategy must be based on very specific, measurable, consistent criteria. A strategy even partly based on subjective evaluations or gut feel cannot be effectively backtested.
- The sample time period must be representative. For example, if your backtesting time sample is from 2010-2020, your test is entirely in bull market conditions. Your results will be skewed and will not predict performance in less favorable markets.
Like all trading methods, backtesting has limitations. You can use it most effectively if you are aware of those limitations.
Choosing Backtesting Tools and Software
There is a wide variety of platforms available for backtesting. These are some of the considerations to look out for when choosing a platform to evaluate your strategy:
- Market coverage. Different platforms cover different markets. Typically a platform will include one or more of the following groups of stocks: US-listed, Canada, US OTC, European, and other international.
- Time period. If you are developing long-term or timeless strategies, you should be able to test them over longer periods of time. Some platforms have data going back decades, while others only have 5 to 10 years of historical data.
- Price action data, fundamental data, or both? You may be building trading strategies based on technical analysis/price action or you may be building investment strategies based on fundamental data. You may also be using a combination of both. This is an area where trading and backtesting tools vary a lot. Some only use price and volume data, while others have a focus on filters like growth and valuation metrics. You will need to find out exactly which data the platform includes, and which of these filters can be used in a backtest.
- How sophisticated can a strategy be? Following on from the previous point, there is also a considerable difference between different platforms when it comes to building a strategy. Some stock screeners allow you to evaluate the profitability of a set of filters, while others allow for full strategy creation. Backtesting a set of filters can be informative for long-term investors but has its limitations. To evaluate a proper trading strategy, you need full control of entry and exit criteria. Each platform differs with regards to how sophisticated a strategy can be, and it really depends on how simple or complex you want the system to be.
Choosing the right platform for your needs is an important first step toward an effective backtesting experience.
How to Get the Most Out of Backtesting Tools
Backtesting should always be done on separate data samples. This is known as in-sample and out-of-sample testing. Initial tests are done on the first sample, and then the strategy is evaluated on the next sample. For results to be valid, there should be consistency between the in-sample and out-of-sample results. When results are consistent and indicate an edge, the strategy is then evaluated with paper trading and finally with live trading.
As much as possible, choose your sample periods so that both the in-sample and out-of-sample data cover a variety of market conditions. Most backtests have an element of survivorship bias built into them because delisted and suspended stocks are not included in the test data. Wherever possible you should try to include these stocks when using backtesting tools.
A good trading strategy is as much about risk management as it is about profits. It’s important to learn about the various risk management metrics. Strategies that tend to be robust and endure over time are often quite volatile. By contrast, strategies that have low volatility often break down when traded live. You should be prepared to accept some volatility if you want a strategy that continues to be profitable.
