volatility-modeling
Volatility Modeling equips traders with methods to measure price fluctuations and predict future market swings using GARCH, EWMA, and realized volatility techniques. The skill covers regime classification, position sizing, and stop placement strategies tailored to crypto's 50–150% annualized volatility levels.
Volatility Modeling estimates and forecasts market volatility using GARCH, EWMA, and realized volatility methods.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-06-24
Volatility Modeling estimates and forecasts market volatility using GARCH, EWMA, and realized volatility methods. Volatility Modeling equips traders with methods to measure price fluctuations and predict future market swings using GARCH, EWMA, and realized volatility techniques. The skill covers regime classification, position sizing, and stop placement strategies tailored to crypto's 50–150% annualized volatility levels.
Use it when
- Volatility Modeling enables inverse position sizing: when volatility is high, you reduce position size; when it's low, you can size larger.
- Volatility Modeling analyzes volatility cones to determine whether current volatility is historically high or low.
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agiprolabs/claude-trading-skills/volatility-modeling · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What methods does volatility-modeling use for GARCH volatility forecasting?
Volatility Modeling provides GARCH, EWMA, and realized volatility techniques to forecast price swings. GARCH captures volatility clustering—the tendency for large moves to follow large moves. EWMA applies exponential weighting to recent price changes, giving more influence to recent data. Realized volatility aggregates intraday price movements into a direct historical measure. Together, these methods let you predict future market swings across different market conditions.
How does volatility-modeling help with position sizing based on volatility?
Volatility Modeling enables inverse position sizing: when volatility is high, you reduce position size; when it's low, you can size larger. This keeps your dollar risk constant across market regimes. The skill also covers ATR stop placement strategy, letting you set stops proportionally to current volatility so they're tight in calm markets and wider during turbulent periods, reducing whipsaws.
What is volatility cone analysis and how does volatility-modeling use it?
Volatility Modeling analyzes volatility cones to determine whether current volatility is historically high or low. A cone plots realized volatility at multiple lookback periods (e.g., 10, 20, 60 days) to show the range of typical values. When current vol sits at the cone's top, you're in a high-volatility regime; at the bottom, a low-volatility regime. This context helps you choose appropriate strategies.
How does volatility-modeling classify market regimes and detect vol shifts?
Volatility Modeling classifies market regimes and detects volatility regime shifts to inform strategy selection. By tracking GARCH, EWMA, and realized volatility trends, the skill identifies when the market transitions from calm to turbulent or vice versa. Regime classification lets you switch between mean-reversion strategies (suited to low-vol regimes) and trend-following strategies (suited to high-vol regimes).
What crypto-specific volatility characteristics does volatility-modeling address?
Volatility Modeling addresses crypto's unique volatility profile: annualized volatility typically ranges from 50–150%, far exceeding traditional equities. The skill covers volatility clustering in crypto, tail risk patterns, and crypto market volatility characteristics. These insights help you calibrate position sizes, stop levels, and regime thresholds to crypto's extreme swings rather than applying equity-market assumptions.
Can volatility-modeling calculate daily volatility and percentile rankings?
Yes. Volatility Modeling covers daily volatility calculation methods including Parkinson high-low range volatility and Garman-Klass OHLC volatility, which use intraday price ranges for more efficient estimates. The skill also supports volatility percentile historical analysis, letting you rank today's volatility against its past distribution. This percentile context helps you gauge whether current conditions are extreme or typical.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
Volatility Modeling
Volatility — the magnitude of price fluctuations — is arguably the single most important quantity in trading. It drives position sizing, stop placement, option pricing, and regime detection. This skill covers estimation, forecasting, and practical application of volatility in crypto markets.
Why Volatility Matters
| Use Case | How Volatility Is Used |
|---|---|
| Position sizing | Scale position inversely with vol so each trade risks a consistent dollar amount |
| Stop placement | ATR-based stops widen in high-vol regimes, tighten in low-vol |
| Strategy selection | Mean-reversion works in low vol; momentum works in high vol |
| Risk budgeting | Vol-target portfolios maintain constant portfolio-level risk |
| Regime detection | Vol regime shifts signal changing market dynamics |
| Option pricing | Implied vs realized vol gap creates trading opportunities |
Types of Volatility
Historical (Realized) Volatility
Computed from observed past returns. The
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skills/volatility-modeling/SKILL.md
skills/volatility-modeling/references/estimators.md
skills/volatility-modeling/references/volatility_cones.md
skills/volatility-modeling/scripts/estimate_volatility.py
skills/volatility-modeling/scripts/vol_forecast.py
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Related skills
This skill teaches time-series volatility modeling and options-market approaches to quantify and predict market uncertainty. Learn EWMA and GARCH frameworks for volatility forecasting, understand implied volatility extraction from option prices, and interpret volatility smile, skew, and term structure patterns. Use it to calculate volatility risk premiums, analyze the VIX, and support derivatives pricing and portfolio hedging decisions.
Regime Detection classifies market conditions across volatility and trend axes to help you choose the right strategy for current conditions. It combines simple approaches like ATR percentiles and ADX with statistical methods including Hurst exponent and change-point detection, with tuning for crypto's faster regime shifts.
This strategy captures volatility mean reversion by computing annualized historical volatility and ranking it percentile-wise over a lookback period. Long signals trigger when volatility sits in the low percentile range, positioning for expansion; short or exit signals fire when volatility ranks high, betting on contraction. Works with any OHLCV data across equities and crypto.
Build volatility forecasts using GARCH(1,1) and GJR(1,1) models with walk-forward fitting, then apply them to scale positions inversely to risk. The toolkit includes tested recipes for univariate fits, DCC correlation overlays, and position-sizing logic on BTC, ETH, SOL, and AVAX futures data. Results are cost-dependent: GJR vol-scaling works at institutional rates (2bps) but erodes at retail spreads (7bps); DCC de-weighting adds minimal economic value.
Position Sizing provides five methods to determine trade size based on account risk, volatility, edge probability, and market liquidity. Fixed fractional sizing—risking a set percentage per trade—is recommended for most traders, while Kelly criterion applies mathematical optimization for quantified edges. The skill combines all methods to apply the most conservative constraint, ensuring your position size aligns with your risk tolerance and market conditions.
Mean-reversion identifies when prices, spreads, or other financial metrics deviate from their long-run average and predictably return. This skill provides statistical tests (ADF, Hurst exponent, variance ratio) to confirm mean reversion, half-life estimation to time entries and exits, z-score frameworks for signal generation, and Ornstein-Uhlenbeck process modeling for continuous-time analysis.
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