Python Quant Developer for Liquidity-Based Market Structure Analysis
We're looking for an experienced Python developer with quantitative trading and/or algorithmic backtesting experience to build a modular Liquidity-Based market structure analysis. This system will analyze historical OHLCV data (mostly crypto) and detect specific SMC patterns across multiple timeframes, then calculate a layered probabilistic model to evaluate the strength of trading setups. You must be comfortable working with: Python Pandas / vectorized operations Multi-timeframe pattern detection Basic market structure concepts Clean, modular system design You will create independent Python modules/classes for: HOB (Higher Order Block) detection OB (Order Block) detection FVG (Fair Value Gap) detection Liquidity sweeps (equal highs/lows, double tops/bottoms) Swing highs/lows BOS (Break of Structure) CHoCH (Change of Character) Displacement candle detection HTF Context Identify HTF premium/discount zones Detect nested OB/FVG setups Multi-timeframe alignment logic Major Chart Confluence System must parse and analyze major charts such as: BTC.D TOTAL TOTAL2 USDT.D / USDC.D ETHBTC SPX / DXY Must-Have Skills Strong Python + Pandas Experience with OHLCV datasets Understanding of indicators or market structure Ability to write modular, scalable code Clear communication + documentation Experience with crypto markets Familiarity with Smart Money Concepts (SMC) Experience with backtesting frameworks Numpy vectorization skills TradingView PineScript experience (not mandatory) Apply tot his job