qjtrader 0.4.0

Official Python client for the QJ Trader AI Trading APIs — Canadian market data and order entry.

qjtrader

Official Python client for the QJ Trader AI Trading APIs — stream real-time Canadian market data and send orders to Canadian venues (Montréal Exchange derivatives, and equities across every lit exchange and dark pool) over one authenticated connection.

pip install qjtrader

• Free sandbox, no approval. Create an account at gateway.qjtrader.ai, click Create sandbox credential, and you get a client_id + client_secret that stream simulated data and return simulated fills — in the exact production wire format, 24/7.
• Sandbox → production with one credential swap. Your code never changes; the credential decides sandbox vs. real, server-side.
• Stdlib only. No dependencies — easy to install, easy to audit.

Quickstart

Get a sandbox key from the console, then:

export QJ_CLIENT_ID="your-client-id"
export QJ_CLIENT_SECRET="your-client-secret"

Send an order

import qjtrader

client = qjtrader.Client()  # reads QJ_CLIENT_ID / QJ_CLIENT_SECRET from the environment

with client.orders() as oe:
    fill = oe.order_and_wait(
        sym="MX:CRAU26", side="buy", qty=1, price=97.00, account="SIM", tif="ioc",
    )
    print(fill)   # {'type': 'exec', 'status': 'filled', 'last_px': 97.0, 'cum_qty': 1, ...}

Lower-level, if you want every message:

with client.orders() as oe:
    cid = oe.order(sym="MX:CRAU26", side="buy", qty=1, price=97.00, account="SIM")
    for msg in oe.updates(timeout=10):
        print(msg)          # accepted -> new -> (partial)* -> filled | canceled | replaced
    oe.cancel(cid)
    print(oe.status())      # open orders + session state

Stream market data

import qjtrader

client = qjtrader.Client()

with client.market_data() as md:
    md.subscribe(["CA:RY", "CA:RY.PT", "MX:CRAU26"], depth=5)
    for msg in md.messages(timeout=30):
        if msg["type"] == "quote":
            print(msg["symbol"], msg["data"]["bid"], msg["data"]["ask"])

• CA:RY is the consolidated Canadian equity book (each level tagged with its venue); CA:RY.PT is PURE (CSE) only. Futures like MX:CRAU26 are venue-native. See the full symbology reference.

Command line

The package installs a qjtrader command:

qjtrader subscribe CA:RY MX:CRAU26 --watch 30
qjtrader order --sym MX:CRAU26 --side buy --qty 1 --price 97.00 --account SIM --tif ioc
qjtrader status
qjtrader cancel --orig qj-abc123

# strategies: the same file runs in backtest and live
qjtrader backtest examples/strategy_meanreversion.py --symbol MX:CRAU26 --bars 200
qjtrader run       examples/strategy_meanreversion.py --symbols MX:CRAU26 --tag mr1

Strategies — one contract, every venue

Subclass Strategy and the same file runs in the backtest engine, a paper run, or live (plan §10). Backtests are offline and deterministic (no network, no secrets); qjtrader run hosts it against a live/paper credential, tags every order with the strategy name (so the journal groups by strategy), and cancels everything on Ctrl-C.

from qjtrader import Strategy, run_backtest, synthetic_bars

class Buy2Percent(Strategy):
    def on_bar(self, ctx, bar):
        if ctx.position(bar["symbol"]) == 0 and bar["close"] < ctx.param("floor", 0):
            ctx.buy(bar["symbol"], 1, bar["close"], tif="ioc")
    def on_fill(self, ctx, fill):
        ctx.log("filled", fill.get("cid"), "@", fill.get("last_px") or fill.get("price"))

report = run_backtest(Buy2Percent(), synthetic_bars("MX:CRAU26", 200), params={"floor": 95})
print(report["total_pnl"], report["positions"])

The bar-level backtester is for logic; L2 event-replay with queue-model fills (microstructure truth) comes from the paper environment.

Configuration

Client() reads these (constructor args override environment):

Setting	Env var	Default
Client ID	QJ_CLIENT_ID	— (required)
Client secret	QJ_CLIENT_SECRET	— (required)
Token endpoint	QJ_TOKEN_URL	QJ Cognito token URL
Market-data host	QJ_DATA_HOST	data-feed.qjtrader.ai:7000
Order-entry host	QJ_ORDERS_HOST	orders.qjtrader.ai:7001
Pinned CA/cert	QJ_CA_FILE	none (standard public-CA validation)

Tokens are minted for you (OAuth2 client-credentials) and refreshed automatically before they expire — you never handle them directly. Need a raw token (e.g. for the WebSocket interface)? client.token(qjtrader.MARKET_DATA_SCOPE).

Pilot note: while order entry is in private pilot it may be reached by IP with a pinned certificate provided at onboarding — pass ca_file="pilot-server.pem" (or QJ_CA_FILE). Market data uses a standard public certificate.

How it works

Both APIs speak NDJSON over TLS — one JSON object per line, UTF-8, newline-terminated, authenticated with an OAuth2 JWT sent on the first line. The order lifecycle is a deterministic, journaled state machine (accepted → new → (partial)* → filled | canceled | replaced), commands are idempotent per client order id (cid), and the server enforces pre-trade risk checks + cancel-on-disconnect. Full protocol: Order Entry and Market Data.

Use it from an LLM (MCP)

Prefer to drive QJ from Claude or another AI assistant? The companion qjtrader-mcp server exposes these APIs as Model Context Protocol tools — subscribe to quotes and place simulated orders in plain language, no code. Order tools refuse a live credential by default (sandbox-only unless you opt in). Add it to Claude Code with:

claude mcp add qjtrader -e QJ_CLIENT_ID=... -e QJ_CLIENT_SECRET=... -e QJ_ENV=sandbox -- uvx qjtrader-mcp

The console's "Connect your AI" panel generates this for you, pre-filled.

Links

• 📚 Docs: https://docs.qjtrader.ai/docs/ai
• 🔑 Console (get a key): https://gateway.qjtrader.ai
• 🔤 Symbology: https://docs.qjtrader.ai/docs/ai/symbology
• 🐛 Issues: https://github.com/QJTrader/qjtrader-python/issues

License

Apache-2.0. See LICENSE.