Backtesting is a crucial part of developing successful trading strategies. It’s how we simulate our strategies using past market data to gauge how they would have performed. But building a backtesting pipeline that gives you actionable insights can be tricky, especially if you’re just starting out. In this post, I’ll take you through the exact backtesting pipeline I use to test and optimize my trading strategies.
Step 1: Initial Research with VectorBT
I start with VectorBT, a Python library that’s excellent for initial research and validation of strategies. It allows me to quickly explore strategies, calculate performance metrics, and analyze results. I use it because it’s fast, flexible, and perfect for exploring different ideas.
Getting Started with Historical Data
For each strategy, I begin by gathering one year of historical data from selected asset classes. I pick a mix of assets—stocks, forex, crypto, etc.—to see how the strategy holds up across different market conditions. The data is cleaned and pre-processed, and I make sure to account for important variables such as spread, slippage, and transaction costs.
VectorBT makes it easy to load and manipulate this data, giving me a great starting point for testing my ideas.
Step 2: Synthetic Data for Robustness Testing
Once I’ve run the strategy on historical data, I move on to synthetic data testing. This is an important step in checking the robustness of the strategy.
Synthetic data is generated using models like random walks or bootstrapping, which mimic real-world market conditions. The idea here is to see if the strategy can handle randomness and adapt to various unforeseen scenarios. If a strategy performs well on synthetic data, it’s a good sign that it’s not overfitting to the past and might have some real-world potential.
Step 3: Walk-Forward Testing
If the strategy shows promising results during the synthetic data phase, I then move on to walk-forward testing. This involves testing the strategy on unseen data in a way that mimics real-time trading.
In walk-forward testing, I divide the historical data into segments, usually rolling windows, and test the strategy over each segment. The idea is to make sure the strategy can perform in future, unseen market conditions. If the strategy continues to deliver solid results after this testing phase, I start to get excited about its potential.
Documentation: Tracking Every Step
Throughout this process, I document everything—each decision, test result, and tweak I make. Keeping track of everything helps me identify what works, what doesn’t, and where I can improve. This step also helps me when I need to go back and analyze why something didn’t work or understand how a strategy evolved.
Step 4: Event-Driven Testing with MetaTrader 5
If the strategy shows solid performance after walk-forward testing, I move on to event-driven testing. For this phase, I use MetaTrader 5 (MT5), which is my preferred platform for deploying strategies. MT5 allows me to test how a strategy performs when real market events occur, such as news releases or sudden market volatility.
During this phase, I simulate live market conditions and check how my strategy reacts to various events. This phase ensures that my strategy isn’t just solid during stable periods but can also handle unpredictable market movements.
Key Performance Metrics I Look At
To evaluate the effectiveness of my strategies, I use several performance metrics. These help me understand how well my strategies are performing, how much risk is involved, and whether they’re worth deploying live. Some key metrics I always check include:
-
Sharpe Ratio: This is a measure of risk-adjusted return. The higher the Sharpe ratio, the better the strategy’s return relative to the amount of risk taken. I typically look for a Sharpe ratio above 1, which indicates that the strategy is generating returns that justify its risk.
-
Drawdown: This is the largest peak-to-trough decline in the equity curve. It shows how much of a loss you would experience from the highest point to the lowest point in the strategy’s performance. Keeping drawdown low is crucial for long-term sustainability.
-
Win Rate: This is the percentage of trades that are profitable. While a high win rate is appealing, it’s important to balance it with other metrics like risk/reward ratio to avoid overfitting.
-
Profit Factor: The ratio of gross profits to gross losses. A value greater than 1 indicates that the strategy is profitable, and the higher the value, the more profitable it is.
Step 5: Moving On If It Doesn’t Show Promise
Not every strategy will make it through the pipeline, and that’s okay! If a strategy doesn’t show promise during any of these stages, I move on and start searching for the next idea. There’s no point in continuing to test a strategy that’s unlikely to succeed.
Conclusion
Backtesting is a continuous process that takes time and careful attention to detail. By using tools like VectorBT and MetaTrader 5, I can quickly iterate and refine my strategies to ensure they’re robust and ready for real-world trading. I always focus on simplicity, adaptability, and consistency—if a strategy doesn’t perform well under different market conditions, I move on to the next one.
If you’re just getting started with backtesting, my advice is to take it step by step. Document everything, keep things simple, and always test your strategies under a variety of conditions. With persistence and the right tools, you’ll improve your trading and gain more confidence in your strategies.
Want to Learn More?
If you found this post useful, subscribe to my newsletter for more insights on backtesting, algorithmic trading, and performance optimization. I’ll be sharing tips and strategies to help you level up your trading game!