session behavior profiler

The problem

Traders often struggle to quantify how market behavior shifts between sessions, leading to strategies that fail during low-volume lunch hours or high-volatility opens. Relying on gut feeling about session characteristics lacks the empirical backing needed for systematic backtesting.

What it does

• Segments OHLCV time series data into Asia, London, New York, lunch, and close windows.
• Calculates normalized range profiles including ATR percentiles and expansion/contraction classifications.
• Analyzes volume intensity relative to daily averages and session-specific benchmarks.
• Quantifies directional bias using body-to-wick ratios and R-multiple net moves.
• Measures continuation probabilities to identify persistence or reversal tendencies between sessions.

Frameworks & tools

Works with standard OHLCV data structures, CSV exports, or JSON time series. Applicable for Python (Pandas/NumPy) or TypeScript financial analysis environments.

Why this beats prompting it yourself

Manually prompting for session analysis often results in vague qualitative descriptions. This skill enforces a strict quantitative framework, calculating specific metrics like body ratios and annualized realized volatility that generic LLMs miss without deep financial engineering instructions.

Use cases

• Identify which sessions have the highest probability of filling overnight gaps.
• Adjust risk parameters based on the volatility state of the upcoming session.
• Filter trade signals that occur during low-intensity volume phases like the lunch window.
• Optimize entry timing by aligning with session-specific directional consistency.

Known limitations

Profiles are empirical observations of historical data, not predictive signals. Requires structured OHLCV data with clear timezone context for accurate session segmentation.