How Algorithmic Traders Backtest Their Automated Strategies

Backtesting is an analytical approach that allows traders to test a strategy over historical data. Many experts have touted this approach as necessary for successful trading because it helps understand how different strategies work under various market conditions. While many trading platforms provide backtesting tools, algorithmic traders often require more efficient tools.

Algorithmic Trading

Algorithmic trading, or automated trading, uses computer programs (algorithms) to execute trades based on specific instructions. It minimizes human errors by automating trades based on pre-defined instructions and signals from mathematical formulas (indicators). Algorithmic trading strategies include trend-following, arbitrage, index fund rebalancing, or volume- and time-weighted average price executions.

For example, a trader can create a program with instructions to execute sell trades when the Relative Strength Index (RSI) indicator reaches 80, indicating a strong oversold condition with a confirmation on the moving average indicator. Algorithmic traders have the advantage of speed, the absence of human emotions, and strict adherence to trading rules.

Why Algorithmic Traders Backtest Strategies

Evaluation is the primary goal of backtesting. Traders backtest to find out their strategies’ limits and potential errors and to compare strategies. A by-product of backtesting is increased confidence when trading, as it helps traders become more comfortable with the market.

Key Features of Backtesting Tools

• Strategy Development and Visualization: Backtesting tools should allow traders to develop and edit strategies and parameters and provide tools to visualize results.
• Performance Metrics: A good backtesting tool offers performance metrics, such as profit and loss returns, drawdowns, margin and leverage levels, etc. These metrics help traders identify loopholes and potential returns under varying conditions.
• Data Quality and Availability: Market data should be available and of high quality to prevent errors due to incorrect price and volume.
• Customization and Flexibility: Backtesting involves tweaking parameters to observe how strategies perform when conditions change. A good backtesting tool should allow traders to customize parameters and offer excellent flexibility.
• Realistic Simulation and Multi-Asset Support: Algorithmic backtesting tools should support scripting and offer a simulation of market movement using the supplied parameters. They should also support backtesting across several assets and markets, so trades are not limited to a few.

Popular Algorithmic Backtesting Tools

These are some of the most popular tools that algorithmic traders use for backtesting.

TradingView Pine Script

Pine Script is the native programming language of the TradingView platform. It enables developers to build, test, and deploy algorithms and technical indicators. Traders can build trading programs with Pine Script if they have the technical skills, but they can also hire developers to do the technical work, or simply buy pre-built ones.

Creating a Pine Script algorithm involves four steps:

1. Create a new script.
2. Write the trading strategy using Pine Script’s syntax and built-in functions. Traders can also create their functions.
3. Add indicators such as the RSI, Bollinger Bands, Moving Averages, etc.
4. Save and use the strategy.

To backtest a strategy, traders can use the TradingView Bar Replay function to set parameters, choose start and end dates, and replay market price movements.

Programming Frameworks and Libraries

Python, MATLAB, R, and other languages used for numerical computation and data analysis enable traders to build and deploy trading algorithms. Traders can backtest Python libraries and frameworks like Zipline, Vectorbt, Ta-Lib, Backtrader, and Pyalgotrade. These are designed explicitly for backtesting, supporting papers, and live trading.

The advantage of these libraries is that they are usually open source, free, and built for a specific purpose. Open-source tools provide traders with free tools that are constantly upgraded to improve efficiency and expand functionalities.

MetaTrader 4 (MT4) and MetaTrader 5 (MT5)

Metatrader 4 and Metatrader 5 are other trading platforms offering Strategy Tester tools for traders to test their Expert Advisors. Like TradingView, MT4 and MT5 have their native programming languages, MQL4 and MQL5. Traders can build their expert advisors using the MQL scripts or customize pre-built algorithms to execute trades.

While the apps are widely used for trading, they are limited to forex and CFDs backtesting, making them unsuitable for stock or commodity traders.

Common Backtesting Mistakes to Avoid

Four main mistakes impact the quality of backtesting. These are:

• Poor data quality: Data quality is crucial because all backtesting depends on historical data. Traders must ensure they’re using accurate data from their brokers to avoid discrepancies that affect backtesting results.
• Not backtesting enough: One objective of backtesting is to collect as much information about a strategy as possible under various conditions. The ideal approach is to backtest multiple market trends and conditions across at least five years of market data.
• Ignoring market conditions: Ignoring slippages and commissions inflates profits and could be a source of error when backtesting. Traders need to consider the transaction costs for every trade to know whether their strategies can remain profitable if conditions change. For example, periods of high volatility and high spreads can affect breakout strategies.
• Overfitting: Backtesting aims to understand a strategy, its limitations, and possibilities. Traders use backtesting results to fine-tune strategies, but may risk overdoing it. Overfitting focuses on market noise instead of real asset price movements and can lead to failure in live markets. To avoid this, traders should focus on a few parameters, such as price and volume, at a time.

Avoiding these mistakes helps improve backtesting quality and enables traders to make data-based decisions that are void of human emotions.

Closing Thoughts

Algorithmic trading is often more efficient than human trading because it focuses on the market and cuts off emotional trading. Backtesting automated algorithms helps traders gain new perspectives and confidence in their strategies, but it must be done right. Tools like the native TradingView Pine Script, Python libraries, and the MT4/MT5 strategy tester allow extensive backtesting for algos.