How to Perform Derivatives Trading Using Jupyter Notebook

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Derivatives trading has become increasingly accessible through powerful programming tools like Jupyter Notebook, especially when combined with advanced trading platforms such as OKX. This guide walks you through how to seamlessly integrate Jupyter Notebook with the OKX API to execute, monitor, and manage derivatives trades — all from a single, interactive environment.

Whether you're a data scientist, algorithmic trader, or crypto enthusiast, this tutorial equips you with practical code examples and essential insights into perpetual swaps, leverage settings, position modes, and real-time order management.

Understanding Derivatives on OKX

OKX supports three primary types of derivatives for traders:

In this guide, we’ll focus on Perpetual Swaps, one of the most popular instruments due to their lack of expiration and flexible leverage options. However, the same principles apply across other derivative types with minor adjustments.

👉 Get started with real-time derivatives data in your notebook today.

Core Tools: Setting Up the OKX API in Jupyter

To interact with OKX’s derivatives market via Jupyter Notebook, you’ll use the official okx Python SDK. This allows you to retrieve market data, check balances, place orders, and manage positions programmatically.

Prerequisites:

Step-by-Step: Key Functions for Derivatives Trading

1. Retrieve Real-Time Market Data

Stay updated with current market conditions by fetching live tickers for perpetual contracts.

import okx.MarketData as MarketData

flag = "1"  # Use "1" for demo trading, "0" for live
marketDataAPI = MarketData.MarketAPI(flag=flag)
result = marketDataAPI.get_tickers(instType="SWAP")  # SWAP = Perpetuals
print(result)

Replace "SWAP" with "EXPIRY" or "OPTION" to get data for futures or options markets.

2. List Available Trading Pairs

Discover which perpetual contracts are available for trading.

import okx.PublicData as PublicData

publicDataAPI = PublicData.PublicAPI(flag=flag)
result = publicDataAPI.get_instruments(instType="SWAP")
print(result)

Calculate Notional Value of a Contract

Each derivative contract has a notional value based on its ctVal (contract value) and ctMult (contract multiplier):

Notional Value = ctVal × ctMult (in ctValCcy)

For example, the LTC-USD-SWAP contract:

{
  "instId": "LTC-USD-SWAP",
  "ctVal": "10",
  "ctMult": "1",
  "ctValCcy": "USD"
}

→ Notional value = 10 × 1 = 10 USD per contract

This helps assess exposure and risk before placing trades.

3. Check Your Account Balance

Monitor your available funds across all currencies.

import okx.Account as Account

accountAPI = Account.AccountAPI(api_key, secret_key, passphrase, False, flag)
result = accountAPI.get_account_balance()
print(result)

Ensure sufficient margin is available before initiating leveraged positions.

4. Verify Eligible Account Mode for Derivatives

Only certain account modes support derivatives trading:

Spot-only mode does not allow derivatives trading.

Check your current mode:

result = accountAPI.get_account_config()
if result['code'] == "0":
    acctLv = result["data"][0]["acctLv"]
    mode_map = {
        "1": "Simple mode (Spot only)",
        "2": "Single-currency margin",
        "3": "Multi-currency margin",
        "4": "Portfolio margin"
    }
    print("Account Mode:", mode_map.get(acctLv, "Unknown"))

👉 Switch to a derivatives-enabled account mode now.

5. Set Leverage for Higher Efficiency

Leverage amplifies both gains and losses. OKX allows up to 125x leverage depending on instrument and position size.

Key terms:

Example: Setting 5x leverage on BTC-USDT-SWAP:

# Cross-margin mode
accountAPI.set_leverage(
    instId="BTC-USDT-SWAP",
    lever="5",
    mgnMode="cross"
)

# Isolated margin for long position only
accountAPI.set_leverage(
    instId="BTC-USDT-SWAP",
    lever="5",
    posSide="long",
    mgnMode="isolated"
)
⚠️ Always adjust leverage according to your risk tolerance.

6. Place Orders Using Position Modes

Choose between two position modes:

Switch mode via API:

accountAPI.set_position_mode(posMode="long_short_mode")

Place a Limit Order

Buy 100 BTC-USDT-SWAP contracts at $19,000:

tradeAPI.place_order(
    instId="BTC-USDT-SWAP",
    tdMode="isolated",
    side="buy",
    posSide="long",
    ordType="limit",
    px="19000",
    sz="100"
)

Place a Market Order

Execute immediately at current market price:

tradeAPI.place_order(
    instId="BTC-USDT-SWAP",
    tdMode="isolated",
    side="buy",
    posSide="long",
    ordType="market",
    sz="100"
)

7. Monitor and Manage Orders

Get Order Details

tradeAPI.get_order(instId="BTC-USDT-SWAP", ordId="505073046126960640")

Cancel an Order

tradeAPI.cancel_order(instId="BTC-USDT-SWAP", ordId="505073046126960640")

Amend Order Size

tradeAPI.amend_order(
    instId="BTC-USDT-SWAP",
    ordId="505073046126960640",
    newSz="80"
)

8. Retrieve Open and Historical Orders

List open orders:

tradeAPI.get_order_list()

Get past orders (last 7 days):

tradeAPI.get_orders_history(instType="SWAP")

Archive beyond 7 days:

tradeAPI.get_orders_history_archive(instType="SWAP")

9. View Transaction and Position Data

Fetch executed trades:

tradeAPI.get_fills()  # Last 3 days
tradeAPI.get_fills_history(instType="SWAP")  # Last 3 months

Check current positions:

positions = accountAPI.get_positions()
for pos in positions['data']:
    print(f"Symbol: {pos['instId']}, PnL: {pos['upl']}")

Track unrealized PnL (upl) to evaluate active trade performance.

Frequently Asked Questions (FAQ)

Q: Can I use Jupyter Notebook for live derivatives trading?
A: Yes. With proper API keys and risk controls, Jupyter Notebook can be used for both demo and live trading environments.

Q: What is the difference between isolated and cross margin?
A: Isolated margin allocates specific collateral to a single position. Cross margin uses the entire balance as potential collateral, increasing efficiency but also risk.

Q: How do I avoid liquidation in high-leverage trades?
A: Monitor your maintenance margin ratio (MMR), set stop-losses, and avoid over-leveraging relative to your account size.

Q: Can I automate trading strategies using these APIs?
A: Absolutely. You can build automated bots for arbitrage, trend following, or hedging strategies directly within Jupyter.

Q: Is it safe to store API keys in Jupyter notebooks?
A: Never hardcode sensitive credentials. Use environment variables or secure vaults to protect your keys.

Q: Does OKX support backtesting in Jupyter?
A: While OKX doesn't provide built-in backtesting tools, you can download historical data via APIs and use libraries like pandas or backtrader for strategy testing.

Final Thoughts

Jupyter Notebook offers a powerful platform for executing and analyzing derivatives trades programmatically. By integrating it with the OKX API, traders gain full control over market access, order execution, and portfolio monitoring — all within a flexible, scriptable environment.

Whether you're building a simple alert system or a full algorithmic trading engine, combining Python’s analytical strength with OKX’s robust derivatives infrastructure opens new possibilities for precision and automation.

👉 Start coding your next trade strategy in minutes.