Quantitative Trading Developer Needed for Historical Strategy Research & Machine Learning Dataset

I am looking for an experienced Quantitative Trading Developer to help me research and validate a custom futures trading strategy. This is NOT a trading bot project yet. The first objective is to build a high-quality historical research dataset that will later be used for machine learning. My Strategy I have a custom 3-confirmation strategy that generates approximately 20+ trade signals per day. The strategy consists of: * Custom candle pattern * Higher High / Lower Low structure * Absorption confirmation * Delta Footprint * Volume * Heikin Ashi Momentum * Higher Timeframe Momentum * Market Session Context I will explain the exact rules during our meeting. ⸻ Goal For every historical occurrence of my custom strategy, I want to determine: * Did the trade continue immediately? * Did it fail immediately? * Why? The objective is to discover which market conditions consistently separate winning trades from losing trades. ⸻ Historical Data I want to work with 500–1,000+ days of historical futures market data if possible. If another data provider is required, I would like your recommendation. ⸻ Features I Want Collected For every occurrence of my strategy, I would like the dataset to include: Order Flow * Delta * Bid/Ask Volume * Footprint Data * Absorption * Buying vs Selling Pressure * Volume Imbalances * Volume Profile (if available) ⸻ Price Action Previous 10–20 candles before my signal (or another lookback window if you recommend one). For each occurrence capture: * OHLC * Candle Size * Wicks * Momentum * Trend * ATR * Volatility ⸻ Indicators * Heikin Ashi Momentum * RSI * Higher Timeframe Trend * Higher Timeframe Momentum * VWAP Distance * Support / Resistance Distance ⸻ Market Context * Session (New York / London / Asia) * Time of Day * Volatility Regime ⸻ Labels Every signal should be labeled as: * Immediate Continuation * Immediate Failure I also want to know: * Maximum Favorable Excursion (MFE) * Maximum Adverse Excursion (MAE) and whether price reached my target before my stop. ⸻ Deliverables I am looking for: 1. Historical dataset 2. Python code used to generate the dataset 3. Documentation 4. Research report showing: * Which features matter most * Which combinations produce the highest probability trades * Which combinations produce immediate failures 5. Recommendations for improving the strategy. ⸻ Future Project If this phase is successful, I will hire the same developer to build: * Machine Learning Model * Trade Scoring System * TradingView Integration * AI Trade Quality Analyzer Eventually the AI should score every signal like: Trade Quality: 94% Expected Move: +35 ticks Reason: * Strong Delta * High Volume * Higher Timeframe Trend Aligned * Strong Momentum * Low Selling Pressure ⸻ Requirements Please apply only if you have experience with: * Quantitative Trading * Futures Markets * Historical Backtesting * Python * Machine Learning * TradingView * Order Flow * Footprint Data * Delta * Financial Data Engineering When applying, please send examples of similar quantitative trading or market research projects you have completed.