I have an equity-only trading strategy that I follow on daily candles and I want solid historical evidence on how it really performs. The rules themselves are already written out; what I need now is for someone to translate those rules into code, run a backtest on a broad stock universe, and present the results in a clear, actionable format. Key points you should know up front • Instrument: Stocks only (no forex or crypto). • Data granularity: Daily bars throughout the entire test period. • Core metrics: Profit and loss, maximum drawdown, and overall win rate must be reported and visualised. I am comfortable if you use Python with popular libraries such as pandas, backtrader, zipline or QuantConnect, but I am open to other robust solutions you might prefer. Accuracy of the logic is more important than the specific toolkit. Deliverables 1. Well-commented source code that fully reproduces the test. 2. A concise report (PDF or notebook) including the three metrics above plus any supporting charts you feel add clarity. 3. Instructions to rerun the test on my own machine or on a cloud notebook. I will provide the exact entry, exit, and position-sizing rules once we begin. If the initial results look promising I may ask for further optimisation work, so writing clean, reusable code is essential. Let me know your relevant experience with equity backtesting and which platform you propose to use, and we can get started right away.