Profitable Back testing techniques, anyone?

<p id="isPasted">You can improve backtesting by backtesting 10-20 years, 2m bars using tradingview deep backtesting. That will give you a better outlook on the strategies</p>

<p id="isPasted"><strong>1. Avoid common biases</strong></p><p>Prevent look-ahead bias: Use only the data that would have been available at the time of the trade. For example, do not use a stock's closing price to generate a trade signal within the same day.</p><p>Fight survivorship bias: Your data should include delisted or bankrupt companies, not just those that have succeeded over time. Excluding failures can give you an unrealistically positive result.</p><p>Overfitting: Avoid tweaking your strategy's parameters (e.g., changing a moving average from 50 to 51) multiple times on the same historical data to achieve a "perfect" result. A strategy that is overly optimized for past data will likely fail on new data.</p><p>Data snooping: Do not test a huge number of strategies on the same dataset until you find one that appears to work. This can lead to false positives that are unlikely to succeed in live trading.&nbsp;</p><p><strong>2. Simulate realistic trading conditions</strong></p><p>Account for all costs: Subtract realistic transaction costs from your backtest results, including commissions, spreads, and slippage. These costs can turn a theoretically profitable strategy into a losing one.</p><p>Diversify market scenarios: Your historical data should include a variety of market conditions, such as bull markets, bear markets, and sideways markets. A strategy that only works in a bull market is not robust.</p><p>Use clean, reliable data: Garbage in, garbage out. The quality of your data directly affects the accuracy of your backtest. Ensure the data is adjusted for corporate actions like stock splits and dividends.&nbsp;</p><p><strong>3. Validate your results with forward-testing</strong></p><p>Out-of-sample testing: Reserve a portion of your historical data that is completely untouched during the strategy development and backtesting phases. Test your final strategy on this "out-of-sample" data to get a more reliable assessment of its performance.</p><p>Monte Carlo analysis: Perform a Monte Carlo simulation, which runs thousands of variations of your strategy to test its robustness under different sequences of outcomes. This helps evaluate the probability of various results and assess the risk of ruin.</p><p>Paper trading: After a successful backtest, forward-test the strategy by "paper trading" it on live, simulated market conditions. This is the final step before risking real capital and helps build discipline.&nbsp;</p><p><strong>4. Define your criteria and evaluate metrics</strong></p><p>Form a clear hypothesis: Define your trading strategy's entry rules, exit rules, and risk management parameters before you begin testing. Vague rules produce unreliable results.</p><p>Analyze multiple metrics: Do not rely on net profit alone. Analyze other key metrics to understand the strategy's risk profile:</p><p>Maximum Drawdown: The largest peak-to-trough decline.</p><p>Sharpe Ratio: Measures risk-adjusted return relative to a risk-free rate.</p><p>Profit Factor: The ratio of gross profits to gross losses. A value above 1.5 is often a good target.</p><p>Win Rate vs. Risk/Reward: A low win rate can still be profitable with a high risk/reward ratio.</p><p>Expectancy: The average profit or loss per trade.&nbsp;</p>