Proper procedure of backtesting?

<p id="isPasted">A backtest is usually coded by a programmer running a simulation on the trading strategy. The simulation is run using historical data from stocks, bonds, and other financial instruments. The person facilitating the backtest will assess the returns on the model across several different datasets.</p><p>It is also essential that the model is tested across many different market conditions to assess performance objectively. Variables within the model are then tweaked for optimization against several different backtesting measures.</p>

<p id="isPasted">The proper procedure for backtesting involves defining your trading strategy, obtaining high-quality historical data, and then systematically running the strategy against that data to record and analyze the results. This process includes simulating trades with realistic conditions like slippage and transaction costs, analyzing the outcomes to identify strengths and weaknesses, and using the findings to refine the strategy before considering it for live trading.&nbsp;</p><p><strong>1. Define your trading strategy</strong></p><ul><li>Establish clear rules: Specify exact entry and exit conditions (e.g., moving average crossover, stop-loss, take-profit).</li><li>Set parameters: Define position sizing, risk management rules (like risk-reward ratio), and the specific market and time frame you will trade. </li></ul><p><strong>2. Acquire historical data</strong></p><ul><li>Obtain reliable data: Get accurate historical data for the specific asset class and time frame you are testing.</li><li>Ensure data quality: Use a platform that provides clean data, and be aware of any potential issues like gaps or errors in the data set. </li></ul><p><strong>3. Execute the strategy</strong></p><ul><li>Simulate trades: Apply your strategy's rules to the historical data, simulating trade entries and exits.&nbsp;</li><li>Include realistic costs: Account for crucial factors like slippage, spreads, and commissions, as they can significantly impact profitability. Platforms like MetaTrader can often factor these in automatically.&nbsp;</li><li>Avoid bias: When backtesting manually, advance one candle at a time and resist the urge to peek ahead to avoid anticipating future price movements. </li></ul><p><strong>4. Record and track results</strong></p><ul><li>Log key metrics: Record the outcome of each trade, along with statistics such as total profit/loss, win rate, and maximum losing streak.</li><li>Note other factors: Track data like average risk-to-reward ratio, best/worst trading days, and performance across different market sessions. </li></ul><p><strong>5. Analyze the outcomes</strong></p><ul><li>Evaluate performance: Examine the recorded results to understand the strategy's strengths and weaknesses.&nbsp;</li><li>Identify patterns: Look for patterns in the performance, such as which market conditions or time frames the strategy performs best or worst in. </li></ul><p><strong>6. Refine and optimize</strong></p><ul><li>Adjust parameters: Based on your analysis, adjust the strategy's parameters (e.g., change stop-loss values) to see if performance improves.&nbsp;</li><li>Iterate and retest: Go back to step 3 to re-execute the strategy with the new parameters and see how the results change. </li></ul><p><strong>7. Validate the strategy</strong></p><ul><li>Test robustness: After optimizing, test the strategy on a different, unseen period of historical data to ensure it performs consistently and isn't overfit to a specific past market condition.</li><li>Consider live testing: Once you are confident in the strategy's performance, you can consider testing it with a small amount of real capital. </li></ul>