1. What is backtesting?
Backtesting applies a trading strategy to historical data to evaluate its results before committing real money.
💡 Why backtest?
• Evaluate in advance whether the strategy produced historical profits.
• Investigate parameters such as stop percentages and holding periods.
• Understand historical maximum drawdown, MDD.
• Make data-based decisions rather than relying only on intuition.
2. Backtesting tools
For beginners
- TradingView: Test strategies on charts using Pine Script.
- 3Commas: Simulate historical results of bot strategies.
For developers
- Backtrader, Python: Described by the source as the most popular Python backtesting framework.
- Vectorbt, Python: Fast, vectorized backtesting.
- Lean, C#: QuantConnect's open-source engine.
3. Main backtesting measures
4. Backtesting traps
⚠️ Overfitting
A strategy fitted too perfectly to historical data can fail in the future.
• Avoid excessive parameter optimization.
• Validate on a different, out-of-sample period.
• Too few trades cannot support a meaningful statistical conclusion.
5. NOONOO TRADING's process in the source
The original guide states that all NOONOO TRADING strategies are backtested on more than 18 months of historical data. It describes a Numba JIT-accelerated engine testing tens of thousands of parameter combinations and walk-forward validation intended to reduce overfitting.
🃏 The source's tested AI approach
View live results from the AI described as having undergone tens of thousands of backtests.
Start in the bot