I need a comprehensive statistical back-test of my liquidity-based forex strategy, limited to the EUR/USD pair over the last four to five years. The core objective is to see how the rules perform across varying market conditions and to receive a clean data set that I can study further. The raw price feedmust cover the entire period without gaps so slippage and spread assumptions can be modeled accurately. Once the data are in place, please code the entry, exit, and position-sizing logic I will supply. Python with pandas/NumPy is ideal, but Excel VBA or a platform such as MetaTrader 5 is also acceptable as long as the calculations are clearly documented. Deliverables • A reproducible script or project file that loads the data, applies the liquidity rules, and exports results • Trade-by-trade output with time-stamp, direction, size, entry/exit price, and P/L in pips and percent • Summary statistics (CAGR, Sharpe, win rate, max drawdown, average trade, exposure, and any other standard risk metrics) • Equity curve and at least two additional visualizations you feel best highlight the strengths or weaknesses of the approach • The cleaned four-to-five-year EUR/USD data set in CSV or a similar open format Acceptance criteria: your numbers must be fully reproducible on my machine from the files you provide, and the totals in the summary report must match the aggregation of the trade list exactly. Once everything lines up, I will review the results and sign off.