TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones
Install Volatility Modeling
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agiprolabs/claude-trading-skills volatility-modeling --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/volatility-modeling .claude/skills/volatility-modeling && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .claude/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modelingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agiprolabs/claude-trading-skills volatility-modeling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/volatility-modeling .agents/skills/volatility-modeling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .agents/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agiprolabs/claude-trading-skills volatility-modeling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/volatility-modeling .cursor/skills/volatility-modeling && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .cursor/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agiprolabs/claude-trading-skills.git --path skills/volatility-modeling--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agiprolabs/claude-trading-skills volatility-modeling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/volatility-modeling .gemini/skills/volatility-modeling && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .gemini/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agiprolabs/claude-trading-skills volatility-modelingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/volatility-modeling .github/skills/volatility-modeling && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .github/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agiprolabs/claude-trading-skills volatility-modeling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/volatility-modeling .opencode/skills/volatility-modeling && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "volatility-modeling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/volatility-modeling into .opencode/skills/volatility-modeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "volatility-modeling", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
volatility-modeling- GitHub stars
- 410
- Token cost
- ~2.1k tokens
- SKILL.md length
- 773 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 68
- Repo updated
- First seen
- Licence
- MIT
At a glance
Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones
- Works in 6 steps: Close-to-Close (Standard Deviation of… → Parkinson (High-Low Range) → Garman-Klass (OHLC) → …
- Tasks that involve Forecasting and time series
- SKILL.md covers Why Volatility Matters, Types of Volatility, Estimation Methods and Volatility Cones, plus 6 more sections
- Runs Python scripts from its folder; calls uv
What it does
Volatility Modeling is an agent skill from agiprolabs/claude-trading-skills. Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/estimators.md`, `references/volatility_cones.md` and `scripts/estimate_volatility.py`).
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.
When your agent uses it
- Tasks that involve Forecasting and time series
Example prompts
- “/volatility-modeling”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Volatility Modeling loads about 2.1k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 36 tokens; SKILL.md has 773 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 773 words, ~2,070 tokens.
Download SKILL.mdSave it as
.claude/skills/volatility-modeling/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- volatility-modeling
- description
- Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones
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 most common and directly measurable form. Multiple estimators exist with different statistical efficiency.
Implied Volatility
Derived from option prices via Black-Scholes or similar models. Limited in crypto DeFi where liquid options markets are sparse, but available on Deribit for BTC/ETH.
Forecast Volatility
Predicted future volatility from models like EWMA or GARCH. Used for forward-looking position sizing and risk budgets.
Estimation Methods
1. Close-to-Close (Standard Deviation of Log Returns)
The simplest estimator. Compute the standard deviation of log returns and annualize.
import numpy as np
log_returns = np.log(closes[1:] / closes[:-1])
vol_daily = np.std(log_returns, ddof=1)
vol_annual = vol_daily * np.sqrt(365) # crypto trades 365 days- Pros: Simple, widely understood.
- Cons: Uses only close prices — ignores intraday range.
2. Parkinson (High-Low Range)
Uses the daily high-low range, which is ~5x more statistically efficient than close-to-close.
hl_ratio = np.log(highs / lows)
vol_parkinson = np.sqrt(np.mean(hl_ratio**2) / (4 * np.log(2))) * np.sqrt(365)- Pros: More efficient, captures intraday moves.
- Cons: Downward bias with discrete sampling; ignores close-to-close jumps.
3. Garman-Klass (OHLC)
The most efficient single-day OHLC estimator.
hl = np.log(highs / lows)
co = np.log(closes / opens)
gk = np.mean(0.5 * hl**2 - (2 * np.log(2) - 1) * co**2)
vol_gk = np.sqrt(gk) * np.sqrt(365)- Pros: Best efficiency among OHLC estimators.
- Cons: Assumes no drift; sensitive to opening gaps.
4. Yang-Zhang
Combines overnight (close-to-open) and open-to-close components. Handles gaps properly. Less relevant for 24/7 crypto but useful for tokens with sporadic trading.
5. EWMA (Exponentially Weighted Moving Average)
RiskMetrics approach — no parameters to estimate beyond λ.
lam = 0.94 # RiskMetrics default for daily
ewma_var = np.zeros(len(returns))
ewma_var[0] = returns[0] ** 2
for t in range(1, len(returns)):
ewma_var[t] = lam * ewma_var[t - 1] + (1 - lam) * returns[t - 1] ** 2
vol_ewma = np.sqrt(ewma_var) * np.sqrt(365)- λ = 0.94 for daily data (RiskMetrics).
- λ = 0.97 for weekly data.
- Higher λ → smoother, slower reaction to new information.
6. GARCH(1,1)
The workhorse autoregressive volatility model. Captures volatility clustering.
σ²_t = ω + α · r²_{t-1} + β · σ²_{t-1}- ω: long-run variance weight.
- α: reaction to recent shock (typically 0.05–0.15 for crypto).
- β: persistence (typically 0.80–0.90 for crypto).
- α + β < 1: stationarity constraint.
- Long-run variance: ω / (1 − α − β).
Estimated via maximum likelihood. See references/estimators.md for details.
Volatility Cones
Volatility cones show the percentile distribution of realized volatility at different lookback windows, revealing whether current vol is historically high or low.
Construction
- Get 1+ years of daily data.
- For each lookback window (5, 10, 20, 60, 120 days):
- Compute rolling realized volatility.
- Extract percentiles: 5th, 25th, 50th, 75th, 95th.
- Plot percentiles vs window length — the "cone" shape.
- Overlay current realized vol at each window.
Show full SKILL.md (324 more words)Show less
Interpretation
- Current vol > 75th percentile: historically elevated — expect mean reversion.
- Current vol < 25th percentile: historically compressed — expect expansion.
- Cone narrowing at longer windows: vol mean-reverts over longer horizons.
See references/volatility_cones.md for full methodology and worked examples.
Crypto Volatility Characteristics
Crypto vol differs from traditional assets in important ways:
| Characteristic | Detail |
|---|---|
| Level | 50–150% annualized is typical; TradFi equities are 15–25% |
| Clustering | Strong — high-vol days cluster together |
| Weekday patterns | Weekend vol often lower but weekend gaps can be large |
| Volume correlation | Vol and volume are positively correlated |
| Regime dependence | Bull market vol ≠ bear market vol; ranges are different |
| Mean reversion | Vol mean-reverts more reliably than price |
| Tail risk | Fat tails — more extreme moves than normal distribution predicts |
Regime Classification by Volatility
| Regime | Annualized Vol Range | Characteristics |
|---|---|---|
| Low vol | < 40% | Range-bound, mean reversion works |
| Normal vol | 40–80% | Trending possible, balanced strategies |
| High vol | 80–120% | Strong trends or sharp reversals |
| Crisis vol | > 120% | Liquidation cascades, reduced position size |
Volatility Forecasting
EWMA Forecast
Simple and effective. The current EWMA variance estimate is the 1-step forecast. Multi-step forecasts are flat (same as 1-step).
GARCH Forecast
GARCH produces a term structure of variance forecasts:
σ²_{t+h} = V_L + (α + β)^h · (σ²_t − V_L)Where V_L = ω / (1 − α − β) is the long-run variance.
- Short-horizon forecasts reflect current conditions.
- Long-horizon forecasts converge to long-run variance.
- The speed of convergence depends on α + β (persistence).
See scripts/vol_forecast.py for a working implementation.
Practical Applications
Position Sizing with Volatility
# Vol-target position sizing
target_vol = 0.02 # 2% daily portfolio vol target
current_vol = 0.05 # 5% daily asset vol (annualized ~95%)
weight = target_vol / current_vol # = 0.40 → 40% allocationSee the position-sizing skill for complete integration.
ATR-Based Stop Placement
atr_14 = talib.ATR(highs, lows, closes, timeperiod=14)
stop_distance = 2.0 * atr_14[-1] # 2x ATR stop
stop_price = entry_price - stop_distance # for longsVol-Regime Strategy Selection
vol_percentile = current_vol_percentile(token, window=30)
if vol_percentile < 25:
strategy = "mean_reversion"
elif vol_percentile > 75:
strategy = "momentum_breakout"
else:
strategy = "balanced"Files
References
| File | Description |
|---|---|
references/estimators.md | Full derivations and details for all volatility estimators |
references/volatility_cones.md | Cone construction methodology and interpretation guide |
Scripts
| File | Description |
|---|---|
scripts/estimate_volatility.py | Multi-estimator volatility computation with cone analysis |
scripts/vol_forecast.py | EWMA and GARCH forecasting with term structure output |
Related Skills
regime-detection— Classify market regimes using volatility as a key input.position-sizing— Scale positions inversely with volatility.risk-management— Portfolio-level vol targeting and risk budgets.pandas-ta— ATR and Bollinger Bands are volatility-based indicators.custom-indicators— Build crypto-specific volatility indicators.
Dependencies
uv pip install pandas numpy scipyOptional for live data:
uv pip install httpx© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/volatility-modeling of agiprolabs/claude-trading-skills.
- SKILL.md
- references/estimators.md
- references/volatility_cones.md
- scripts/estimate_volatility.py
- scripts/vol_forecast.py
Open the folder on GitHubat commit 981e1d7
Compare with similar skills
Volatility Modeling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Volatility Modeling this skillagiprolabs/claude-trading-skills | 410 | — | ~2.1k | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.6k | 6 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Alphaear Predictorninehills/skills | 281 | 2 repos | ~531 | Automated safety check: Pass | None | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 |
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Questions about Volatility Modeling
What does Volatility Modeling do?
Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones. Volatility Modeling is an agent skill from agiprolabs/claude-trading-skills.
When should I use Volatility Modeling?
Volatility Modeling fits situations like: tasks that involve Forecasting and time series.
How do I install Volatility Modeling in Claude Code?
Run `npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a claude-code`. Or copy the skill folder (skills/volatility-modeling in agiprolabs/claude-trading-skills) into .claude/skills/volatility-modeling in your project. Claude Code loads it when a task matches its description.
How do I install Volatility Modeling in Codex?
Run `npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a codex`. Or copy the skill folder (skills/volatility-modeling in agiprolabs/claude-trading-skills) into .agents/skills/volatility-modeling in your project. Codex loads it when a task matches its description.
Can I use Volatility Modeling in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill volatility-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/volatility-modeling, .gemini/skills/volatility-modeling, .github/skills/volatility-modeling and .opencode/skills/volatility-modeling in your project.
What does Volatility Modeling need to run?
Going by SKILL.md and its folder, Volatility Modeling needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Volatility Modeling access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Volatility Modeling safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Volatility Modeling use?
Volatility Modeling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Volatility Modeling use?
About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3k tokens, read only when the agent opens those files.
What are the alternatives to Volatility Modeling?
Skills that share tags, products or a category with Volatility Modeling: TimesFM Forecasting (google-research/timesfm, 34k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.6k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Alphaear Predictor (ninehills/skills, 281 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Volatility Modeling?
agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.
Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.