Synthdata Volatility Skill

Query and analyze volatility forecasts from Synthdata.co for crypto, commodities, and stock indices.

Setup

Set your API key:

export SYNTHDATA_API_KEY=your_key_here

Quick Start

# Single asset
python3 scripts/synth.py BTC

# Multiple assets comparison
python3 scripts/synth.py BTC ETH SOL --compare

# All assets overview
python3 scripts/synth.py --all

# Monte Carlo simulation (24h max)
python3 scripts/synth.py BTC --simulate --hours 12

Available Assets

Ticker	Name	Category
BTC	Bitcoin	Crypto
ETH	Ethereum	Crypto
SOL	Solana	Crypto
XAU	Gold	Commodity
SPYX	S&P 500	Index
NVDAX	NVIDIA	Stock
GOOGLX	Google	Stock
TSLAX	Tesla	Stock
AAPLX	Apple	Stock

Output Example

==================================================
  BTC — Bitcoin
==================================================
  Price:           $77,966
  24h Change:      🔴 -0.95%
  Current Vol:     58.4% 🟠 [Elevated]
  Avg Realized:    53.3%
  Forecast Vol:    52.2%

Volatility Levels

Level	Range	Emoji
Low	< 20%	🟢
Moderate	20-40%	🟡
Elevated	40-60%	🟠
High	60-80%	🔴
Extreme	> 80%	🔴

Use Cases

1. Market Overview

python3 scripts/synth.py --all

Get a ranked table of all assets by volatility.

2. Trading Signals

• High forecast → Current low: Expect volatility spike
• Low forecast → Current high: Volatility may decrease
• Use for position sizing and options trading

3. Monte Carlo Projections

python3 scripts/synth.py BTC --simulate --hours 24 --paths 1000

Generate probabilistic price ranges using forecast volatility (24h max - Synthdata forecast window).

4. Scheduled Reports

Create a cron job for daily Slack/Telegram forecasts (see examples/use-cases.md).

5. Risk Alerts

Monitor for assets crossing volatility thresholds and trigger notifications.

API Reference

See references/api.md for full API documentation.

Direct API Usage

import requests

resp = requests.get(
    "https://api.synthdata.co/insights/volatility",
    params={"asset": "BTC"},
    headers={"Authorization": f"Apikey {API_KEY}"}
)
data = resp.json()

# Key fields:
price = data["current_price"]
realized_vol = data["realized"]["average_volatility"]
forecast_vol = data["forecast_future"]["average_volatility"]

Integration Ideas

• Polymarket: Use volatility forecasts to inform up/down market bets
• Options: High forecast vol = consider buying options
• Portfolio: Rebalance when aggregate volatility spikes
• Alerts: Notify when forecast differs significantly from realized