{

Step	Description	Tools / Methods	Key Considerations	Example
1	Define strategy rules	Algorithm logic, trading plan	Clear entry, exit, and risk criteria	Moving average crossover
2	Collect historical data	Market data feeds, CSV, APIs	Ensure data quality and completeness	5 years BTC price data
3	Clean and preprocess data	Remove errors, adjust for splits	Normalize, handle missing values	Correct missing candlesticks
4	Implement backtesting engine	Python, Backtrader, QuantLib	Accurate simulation of trades	Use VWAP for order execution
5	Run strategy simulation	Apply rules to historical data	Include slippage, fees, and spreads	Test MA crossover on BTC/USD
6	Analyze performance metrics	Sharpe ratio, drawdown, ROI	Evaluate profitability and risk	Max drawdown 12%, ROI 25%
7	Optimize parameters	Grid search, walk-forward testing	Avoid overfitting	Adjust MA periods for best ROI
8	Validate with out-of-sample data	Separate dataset from training	Confirm strategy robustness	Test optimized MA on new 6 months data
9	Document results	Performance summary, charts	Transparency and reproducibility	PDF report with equity curve
10	Prepare for live deployment	Paper trading or demo account	Monitor for real-world issues	Execute trades with small capital

p>“query”: “backtesting solutions step by step”,
“languages”: “en”
}