GHOSTRATEGY | Results of 2.5 Year Deep Backtesting | Cheat-code #algotrading #backtesting

The ideal trading strategy on TradingView would be one that maximizes profits while minimizing the "max drawdown" and ensuring consistent profits. Here we have the best strategy that have max drawdown below 10%. [TRIAL LINK for next 7 days] https://www.tradingview.com/script/SK... (add it to favourite to use) [TO BUY] https://cosmofeed.com/vig/63dce19af98... 00:00 Introduction 00:15 Banknifty 01:45 NIFTY 50 04:10 Crudeoil 06:00 Reliance 07:20 SBIN 08:45 TATA Motors 09:50 Natural Gas 11:10 Crytpo 14:40 Jindal Steel 15:45 HDFC 17:00 Kotakbank 18:00 TITAN 18:45 Sunpharma What is Deep-Backtesting ? Deep backtesting in TradingView strategy refers to the process of conducting extensive and thorough historical testing of a trading strategy to assess its performance over a significant period. While TradingView offers a basic backtesting feature that allows you to test strategies on historical data, "deep backtesting" typically involves more advanced techniques and analysis to gain a comprehensive understanding of a strategy's strengths and weaknesses. Here are some steps and considerations for deep backtesting in TradingView strategy: Historical Data: Ensure you have access to sufficient historical price data for the asset or market you want to backtest. The quality and length of the historical data are crucial for obtaining accurate results. Strategy Scripting: Write or import your trading strategy code into TradingView's Pine Script editor. Make sure your strategy is well-defined, taking into account entry and exit conditions, position sizing, and risk management rules. Parameter Optimization: Some strategies have adjustable parameters (e.g., moving average periods, stop-loss levels). Deep backtesting may involve optimizing these parameters to find the combination that yields the best results for the given historical period. Out-of-Sample Testing: Divide your historical data into two parts: one for training and parameter optimization and the other for out-of-sample testing. This helps to avoid overfitting the strategy to historical data. Risk Management and Position Sizing: Implement risk management techniques within your strategy, such as position sizing based on a percentage of your account balance or risk per trade. Slippage and Commission: Consider adding slippage and commission costs to simulate real-world trading conditions more accurately. Backtesting Period: Depending on the strategy's frequency and trading style, consider testing over several years or even multiple market cycles to understand its performance under various conditions. Analysis and Performance Metrics: Measure and analyze various performance metrics, such as profit/loss, maximum drawdown, win rate, Sharpe ratio, and risk-adjusted returns. Walk-Forward Testing: For an additional level of validation, consider performing walk-forward testing, which involves periodically updating the strategy and re-optimizing its parameters. Monte Carlo Simulations: Some traders use Monte Carlo simulations to assess how the strategy might perform under different random market scenarios. Visualizations and Reporting: Create visualizations and reports to better understand the strategy's performance and gain insights into its behavior during different market conditions. Remember that past performance does not guarantee future results. While deep backtesting can provide valuable insights, it is essential to exercise caution and use additional methods like forward testing and paper trading before deploying a strategy with real money. Additionally, always be aware of the limitations of backtesting and the risks involved in trading financial markets.