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)
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Apply Now