Trade Perps in
3 Lines of Code

Buys a fixed USD amount at regular intervals. Emotion-free accumulation with configurable schedule and safety limits.

from arthur_sdk import Arthur
import time

client = Arthur.from_credentials_file("creds.json")

# DCA $50 into ETH every hour
while True:
    client.buy("ETH", usd=50)
    time.sleep(3600)

Places layered buy/sell orders across a price range. Buys below mid, sells above. Profits from oscillations in range-bound markets.

from arthur_sdk import Arthur

client = Arthur.from_credentials_file("creds.json")

# Place grid: 5 buy orders below $2000, 5 sells above
for i in range(5):
    client.limit_buy("ETH", qty=0.1,
        price=1900 + i * 40)
    client.limit_sell("ETH", qty=0.1,
        price=2020 + i * 40)

Fetches klines from Orderly API, calculates RSI, buys oversold, sells overbought. Uses the SDK for execution.

from arthur_sdk import Arthur
import requests, numpy as np
client = Arthur.from_credentials_file("creds.json")

# Fetch 4h candles from Orderly API
url = "https://api-evm.orderly.org/v1/public/kline"
r = requests.get(url, params={
    "symbol": "PERP_ETH_USDC",
    "type": "4h", "limit": 100})
closes = [c["close"] for c in r.json()["data"]["rows"]]

# RSI calculation
d = np.diff(closes)
rsi = 100 - 100/(1 + np.mean(d[d>0])/abs(np.mean(d[d<0])))

if rsi < 30: client.buy("ETH", usd=100)
elif rsi > 70: client.sell("ETH", usd=100)

Bollinger Band strategy — buys at lower band, sells at upper. Uses real SDK methods for clean execution.

from arthur_sdk import Arthur
import numpy as np

client = Arthur.from_credentials_file("creds.json")

# Bollinger Bands from 1h closes
mid = np.mean(closes[-20:])
std = np.std(closes[-20:])
price = client.price("ETH")

if price <= mid - 2*std:
    client.buy("ETH", usd=200)
elif price >= mid + 2*std:
    client.sell("ETH", usd=200)