Install
openclaw skills install synth-dataQuery volatility forecasts from Synthdata.co for crypto, commodities, and stocks. Compare assets and run Monte Carlo simulations.
openclaw skills install synth-dataQuery and analyze volatility forecasts from Synthdata.co for crypto, commodities, and stock indices.
Set your API key:
export SYNTHDATA_API_KEY=your_key_here
# 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
| Ticker | Name | Category |
|---|---|---|
| BTC | Bitcoin | Crypto |
| ETH | Ethereum | Crypto |
| SOL | Solana | Crypto |
| XAU | Gold | Commodity |
| SPYX | S&P 500 | Index |
| NVDAX | NVIDIA | Stock |
| GOOGLX | Stock | |
| TSLAX | Tesla | Stock |
| AAPLX | Apple | Stock |
==================================================
BTC — Bitcoin
==================================================
Price: $77,966
24h Change: 🔴 -0.95%
Current Vol: 58.4% 🟠 [Elevated]
Avg Realized: 53.3%
Forecast Vol: 52.2%
| Level | Range | Emoji |
|---|---|---|
| Low | < 20% | 🟢 |
| Moderate | 20-40% | 🟡 |
| Elevated | 40-60% | 🟠 |
| High | 60-80% | 🔴 |
| Extreme | > 80% | 🔴 |
python3 scripts/synth.py --all
Get a ranked table of all assets by volatility.
python3 scripts/synth.py BTC --simulate --hours 24 --paths 1000
Generate probabilistic price ranges using forecast volatility (24h max - Synthdata forecast window).
Create a cron job for daily Slack/Telegram forecasts (see examples/use-cases.md).
Monitor for assets crossing volatility thresholds and trigger notifications.
See references/api.md for full API documentation.
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"]