Volatility-Scaled Trend Research Template
A lightweight Python research scaffold for studying how simple trend signals behave when combined with volatility-scaled exposure across assets and market regimes. This project is educational and exploratory by design. The goal is not return maximization or production deployment, but understanding risk behavior, drawdowns, and regime dependence in trend-following systems.
Motivation
Trend-following strategies are often evaluated primarily on headline returns, despite being highly sensitive to volatility regimes and exposure management. In practice, volatility targeting can materially alter both performance and drawdown characteristics.
This template separates:
Directional intent (trend signal) Risk management (volatility-scaled exposure) so that the impact of exposure control can be studied independently of signal design.
Method Overview
Trend Signal Long exposure when EMA (fast) > SMA (slow) Flat otherwise Volatility Measure Average True Range (ATR), normalized as ATR%
Exposure Scaling
Position size scales inversely with realized volatility Higher volatility → reduced exposure Lower volatility → increased exposure (capped)
Benchmark
Buy & hold of the same asset
Outputs
The template produces three core diagnostics: Equity Curves Strategy vs buy & hold performance Drawdowns Comparative downside behavior and recovery dynamics Volatility & Exposure Relationship between realized volatility and position sizing These views are intended to support interpretation, not performance marketing.
Project Structure volatility-scaled-trend-template/ │ ├── volatility_scaled_trend.py # Main research script ├── README.md # Project overview └── requirements.txt # Dependencies (optional)
Getting Started
Requirements
Python 3.9+ pandas numpy matplotlib ta (technical indicators) Alpaca Market Data API access (paper trading keys)
Installation pip install pandas numpy matplotlib ta alpaca-py
Set Alpaca API credentials as environment variables: export ALPACA_API_KEY="YOUR_KEY" export ALPACA_API_SECRET="YOUR_SECRET"
Running the Template Edit configuration parameters at the top of the script:
SYMBOL = "NVDA" START = "2021-01-01" END = "2026-01-01"
EMA_LEN = 20 SMA_LEN = 50 ATR_LEN = 14 TARGET_ATR_PCT = 0.02
Run: python volatility_scaled_trend.py
Intended Use This project is suitable for: Research experimentation Educational demonstrations Portfolio and regime analysis Extending to other assets or markets It is not intended to be: A trading bot An alpha signal A production-ready system
Key Takeaways (Typical Findings)
Volatility targeting can significantly reduce drawdowns Risk-managed strategies may underperform buy & hold in strong bull markets Exposure management often dominates signal selection Strategy evaluation is incomplete without drawdown analysis
Limitations
No transaction costs or slippage Single-asset backtest No portfolio construction layer Simplified execution assumptions
Results should be interpreted as behavioral insights, not investable performance.
License & Disclaimer This project is released for educational and research purposes only. See the LICENSE file for details.
Acknowledgements Inspired by practitioner literature on trend following, volatility targeting, and risk-managed portfolio construction.