financial-timeseries-analysis
CommunityTurn time-series data into reliable insights.
AuthorRorySullivan1
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Correctly handling price and return series in pandas/numpy to avoid look-ahead bias, misalignment, and improper resampling.
Core Features & Use Cases
- Compute returns correctly: choose simple vs log returns with proper NaN handling and avoiding look-ahead.
- Align and resample: robustly align multiple time-series on a trading-calendar-friendly DatetimeIndex and perform correct period aggregation.
- Volatility and stationarity: estimate trailing volatility (EWMA/GARCH) and check for stationarity without leaking future data.
- Practical example: build features from noisy price data for strategy signals while maintaining timestamp integrity.
Quick Start
Compute simple returns, align two price series on their DatetimeIndex, and estimate trailing EWMA volatility from the data.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: financial-timeseries-analysis Download link: https://github.com/RorySullivan1/claudeBrain/archive/main.zip#financial-timeseries-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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