Crypto Trading Strategy Analysis, Backtesting and Future Automation

Hello, I am looking for an experienced algorithmic trading specialist who can help analyze, test, improve and eventually automate my cryptocurrency trading strategy. I currently have a manually traded strategy based on custom support and resistance levels, trend analysis, price action and various technical indicators. My goal is not to find a "magic profitable bot." My goal is to objectively understand why my strategy performs well under certain market conditions, why it fails under others, and how it can be improved through data-driven analysis. I would like to find answers to questions such as: - Why did a specific trade fail? - Why did price break through a support or resistance level? - Were there volume spikes, trend changes, market structure signals or higher timeframe confirmations that I missed? - Under which market conditions does the strategy perform best? - Under which market conditions does it perform poorly? - Which filters, confirmations or improvements could increase performance? The first phase of the project should include: - Detailed review and understanding of the existing strategy - Evaluation of current entry and exit rules - Optimization of trade entry locations - Improvement of trade exit logic - Testing of Take Profit and Stop Loss rules - Analysis of losing and stopped-out trades - Detailed review of winning and losing trades - Evaluation of trend identification methods - Performance analysis under different market conditions - Identification of missing or ineffective filters - Evaluation of RSI, volume, volatility, market structure, support/resistance and higher timeframe analysis - Data-driven improvement suggestions - Partial redesign of the strategy if necessary - Detailed reporting and explanation of findings I would also like to improve the position management side of the strategy, including: - Trailing Stop functionality - Break-even Stop systems - ATR-based dynamic stop management - Dynamic stop adjustments as profit increases - Partial profit-taking methods - Trend-following exit management - Risk-to-reward optimization - Comparison of different stop management techniques In addition, I would like the strategy to adapt to different market conditions. Examples include: - Prioritizing long trades during strong bullish trends - Prioritizing short trades during strong bearish trends - Using scalp or range-trading approaches during sideways markets - Avoiding trades during unclear or low-probability market conditions - Implementing market regime analysis - Identifying trending, ranging and transition market environments - Applying different trading logic depending on market conditions My long-term objective is to build a professional trading system that can: - Connect to Binance Futures - Execute trades automatically - Send Telegram notifications - Operate 24/7 on a VPS or cloud server - Be monitored and updated remotely - Continuously measure and improve performance I would prefer to work with someone who has experience in: - Algorithmic Trading - Backtesting - TradingView - Binance Futures - Trading Strategy Development - Trading Automation - Crypto Trading Systems - Strategy Optimization - Market Regime and Trend Analysis The points above summarize my current ideas and objectives. However, I am not limited to these concepts alone. I am open to professional suggestions, alternative approaches and improvements that could increase performance, reduce risk and create a more robust trading system. Different trend identification methods, market regime models, entry and exit frameworks, position management techniques, filters or completely different approaches can be considered if they improve results. My goal is not to follow fixed rules at all costs, but to develop the strongest, most sustainable and continuously improvable trading system possible. When applying, please provide examples of similar projects involving strategy analysis, backtesting, optimization or trading automation. Thank you.