Best Backtesting Strategies for Crypto and Stocks (2025 Guide)
# Best Backtesting Strategies for Crypto & Stock Trading
Introduction
Whether you're trading crypto, stocks, or both, using the right backtesting strategy is crucial to developing a system that works. Backtesting enables traders to simulate their strategy on historical data to evaluate how it might perform in live markets.
In this article, we'll explore the best backtesting strategies, their use cases, and how you can implement them effectively to improve your trading outcomes.
📌 Related: If you’re new to the concept of testing, read Backtesting Trading Strategies in Crypto.
What Makes a Backtesting Strategy Effective?
A good backtesting strategy is:
✅ Rule-based: Clearly defines entry, exit, and risk management.
✅ Repeatable: Can be consistently applied to any market or timeframe.
✅ Based on Realistic Assumptions: Includes slippage, commissions, and execution delays.
✅ Performance-Focused: Produces actionable metrics like Sharpe ratio, win rate, drawdown, etc.
🔗 Related: Learn how to run smarter tests in Optimizing Your Crypto Backtesting.
Top Backtesting Strategies to Use
1. Moving Average Crossover (Trend-Following)
One of the most widely used strategies in both crypto and stocks. Traders use a short-term MA (e.g. 50-day) crossing above or below a long-term MA (e.g. 200-day) to generate buy/sell signals.
Why it works: Helps ride strong trends and avoid sideways markets.
Backtesting Tip: Adjust MA periods and test across multiple assets and timeframes.
2. RSI Overbought/Oversold (Momentum Reversal)
Use the Relative Strength Index (RSI) to identify overbought (above 70) or oversold (below 30) conditions.
Why it works: Capitalizes on short-term price exhaustion before reversals.
Backtesting Tip: Combine with volume or trend confirmation indicators.
3. Bollinger Band Breakout
Price breaking above or below the Bollinger Bands can signal volatility expansion or trend continuation.
Why it works: Breakouts often lead to explosive moves.
Backtesting Tip: Measure success rate of breakouts using historical volatility.
4. Support & Resistance Range Trading
Identify key zones of support/resistance and trade reversals within that range.
Why it works: Markets often respect prior price levels.
Backtesting Tip: Use ATR (Average True Range) to set realistic profit/loss targets.
5. Machine Learning-Based Strategy (Quantitative)
Using AI models like random forests, SVMs, or neural networks trained on technical + sentiment data.
Why it works: Capable of identifying non-obvious patterns across large datasets.
Backtesting Tip: Use cross-validation and avoid overfitting.
📌 Related: Learn more about this approach in Role of Machine Learning in Backtesting.
Tips to Get the Most from Strategy Backtesting
✅ Start Simple: Don’t overcomplicate. Build a basic version first.
✅ Test Across Markets: Apply the strategy to BTC, ETH, stocks, and other instruments.
✅ Include Fees & Slippage: Always.
✅ Validate with Forward Testing: Confirm your backtest results on new, unseen data.
🔗 Related: Understand this balance in Backtesting vs Forward Testing.
Conclusion
The best backtesting strategy is the one that fits your trading style, risk tolerance, and market conditions. Whether you're following trends, reacting to momentum, or using machine learning, backtesting allows you to measure what works before putting money on the line.
🚀 Next step: Pick one strategy, test it thoroughly, and optimize it for your goals!
📌 Related: New to backtesting? Start here: What is Backtesting and Optimizing?
