Python Quant Researcher Needed to Validate CRT and VWAP Futures Strategy

Python Quant Researcher Needed to Validate CRT and VWAP Futures Strategy I am looking for a quantitative researcher or Python developer to statistically test a futures trading strategy based on session VWAP, VWAP deviation, and CRT session-range sweeps and reclaims. This project is focused on mathematical validation and signal research. It does not include automated order execution or Tradovate integration. Markets and Sessions The initial research will cover: * YM/MYM during the New York session * Gold GC/MGC during the London session * Gold GC/MGC during the New York session * Silver SI/Micro Silver during the London session * Silver SI/Micro Silver during the New York session The same core strategy framework should be applied across all instruments so that the results can be compared consistently. Research Question The primary question is whether a confirmed CRT session high or low sweep and range reclaim performs better when price is also statistically extended from session VWAP. The analysis should compare: 1. CRT sweep and reclaim alone 2. VWAP statistical deviation alone 3. CRT and VWAP confluence 4. Required Work The selected freelancer will: * Translate the trading concept into objective and testable rules * Define all relevant sessions in Eastern Time * Calculate session VWAP * Calculate VWAP deviation or z-score * Detect session highs, lows, sweeps, and reclaims * Build a reproducible Python backtest * Test each instrument and session separately * Account for commissions, slippage, and realistic entry timing * Use chronological out-of-sample testing * Test nearby parameters to evaluate sensitivity and overfitting * Compare CRT alone, VWAP alone, and combined signals * Provide a trade-level CSV and written performance report * Provide all Python source code and instructions needed to reproduce the analysis One-minute historical data should preferably be used to construct five-minute strategy signals. Required Results Report the following separately for every instrument and session: * Number of trades * Win rate * Profit factor * Net expectancy per trade * Average winner * Average loser * Maximum drawdown * Average adverse excursion * Average favorable excursion * Average holding time * Results after commissions and slippage * Monthly performance * Long versus short performance * Results by time of day * Results by VWAP deviation threshold * Percentage of trades returning to VWAP * CRT-only versus VWAP-only versus combined performance The final report should identify whether the evidence for each market and session is supported, preliminary, inconclusive, or not supported. TradingView Indicator A basic TradingView indicator reproducing the validated logic would be helpful if it can fit within the budget. The indicator would display: * Session VWAP * VWAP deviation bands * CRT session high and low * Sweep and reclaim markers * Confirmed confluence signals * TradingView alerts The quantitative testing and reproducible Python code are more important than visual styling.