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Gold Perpetual Book Imbalance with Proxy Futures Data (AX Exchange)

docs/tutorials/gold_book_imbalance_ax.md

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Gold Perpetual Book Imbalance with Proxy Futures Data (AX Exchange)

This tutorial backtests a top-of-book imbalance strategy on XAU-PERP at AX Exchange using Databento CME gold futures (GC.v.0) mbp-1 quotes as a proxy.

Introduction

Top-of-book imbalance is a microstructure signal: when one side of the BBO holds significantly more resting size than the other, the book is leaning and short-term price often moves toward the thinner side as the heavier side absorbs flow. The shipped OrderBookImbalance strategy fires a fill-or-kill (FOK) limit order against the thicker side every time the ratio between sides clears a threshold and a cooldown has elapsed.

Because the strategy only needs the BBO, it works with mbp-1 (market by price, single best bid/ask) quote data rather than the full L2 book. That keeps source costs down for backtesting.

OrderBookImbalance is a teaching strategy and has no edge.

mermaid
flowchart LR
    subgraph Inputs ["Data"]
        D["Databento mbp-1 quotes"]
    end

    subgraph Engine ["BacktestEngine"]
        L["DatabentoDataLoader"]
        Q["QuoteTick stream"]
        B["L1 OrderBook in cache"]
    end

    subgraph Strategy ["OrderBookImbalance"]
        R{{"larger >= trigger_min_size
AND smaller/larger < ratio
AND cooldown elapsed"}}
        D2{{"bid_size > ask_size?"}}
        BUY["Submit FOK BUY at best ask"]
        SELL["Submit FOK SELL at best bid"]
    end

    D --> L --> Q --> B
    B --> R
    R -->|yes| D2
    D2 -->|yes| BUY
    D2 -->|no| SELL

Why proxy data

AX Exchange is new and not yet covered by Databento. CME GC gold futures are the most liquid gold derivatives globally and provide representative microstructure for backtesting gold strategies. We use the continuous contract GC.v.0 so the file stitches across expiries on the highest-volume contract, mirroring how a perpetual chases liquidity. The stype_in="continuous" parameter resolves the symbol through Databento's continuous mapping at request time. The instrument_id override at load time is safe because the continuous contract maps to a single underlying instrument at any moment.

For a deeper read on the predictive power of book imbalance features, see Databento's blog post on HFT signals with sklearn.

Prerequisites

bash
export DATABENTO_API_KEY="your-api-key"
  • The Databento Python client: pip install databento.

Data preparation

Download CME gold futures quotes

python
import databento as db
from pathlib import Path

data_path = Path("gc_gold_quotes.dbn.zst")

if not data_path.exists():
    client = db.Historical()
    data = client.timeseries.get_range(
        dataset="GLBX.MDP3",
        symbols=["GC.v.0"],
        stype_in="continuous",
        schema="mbp-1",
        start="2024-11-15",
        end="2024-11-16",
    )
    data.to_file(data_path)

This pulls one trading day. The file is reused on subsequent runs.

Load into Nautilus quote ticks

DatabentoDataLoader.from_dbn_file parses the .dbn.zst archive and emits QuoteTick objects. The instrument_id argument overrides the Databento symbology so every tick appears to come from XAU-PERP.AX.

python
from nautilus_trader.adapters.databento import DatabentoDataLoader
from nautilus_trader.model.identifiers import InstrumentId

instrument_id = InstrumentId.from_str("XAU-PERP.AX")

loader = DatabentoDataLoader()
quotes = loader.from_dbn_file(
    path="gc_gold_quotes.dbn.zst",
    instrument_id=instrument_id,
)

Instrument definition

Proxy data needs a manual instrument definition. Price precision and tick size match the CME source data; margin and fee parameters reflect AX conditions.

python
from decimal import Decimal

from nautilus_trader.model.currencies import USD
from nautilus_trader.model.enums import AssetClass
from nautilus_trader.model.identifiers import Symbol
from nautilus_trader.model.instruments import PerpetualContract
from nautilus_trader.model.objects import Price
from nautilus_trader.model.objects import Quantity

XAU_PERP = PerpetualContract(
    instrument_id=instrument_id,
    raw_symbol=Symbol("XAU-PERP"),
    underlying="XAU",
    asset_class=AssetClass.COMMODITY,
    quote_currency=USD,
    settlement_currency=USD,
    is_inverse=False,
    price_precision=2,
    size_precision=0,
    price_increment=Price.from_str("0.01"),
    size_increment=Quantity.from_int(1),
    multiplier=Quantity.from_int(1),
    lot_size=Quantity.from_int(1),
    margin_init=Decimal("0.08"),
    margin_maint=Decimal("0.04"),
    maker_fee=Decimal("0.0002"),
    taker_fee=Decimal("0.0005"),
    ts_event=0,
    ts_init=0,
)

Fees are explicit backtest assumptions. Check AX documentation for current rates.

Strategy configuration

use_quote_ticks=True and book_type="L1_MBP" together tell the strategy to consume quotes and maintain its own L1 book in cache rather than subscribing to L2 deltas.

ParameterValueDescription
max_trade_size10Cap on contracts per FOK order.
trigger_min_size1.0Larger side must hold at least one contract.
trigger_imbalance_ratio0.10Trigger when smaller / larger < 10%.
min_seconds_between_triggers5.0Cooldown between consecutive triggers.
book_typeL1_MBPTop of book only.
use_quote_ticksTrueDrive the strategy from quote ticks.
python
from nautilus_trader.examples.strategies.orderbook_imbalance import OrderBookImbalance
from nautilus_trader.examples.strategies.orderbook_imbalance import OrderBookImbalanceConfig

strategy = OrderBookImbalance(
    OrderBookImbalanceConfig(
        instrument_id=instrument_id,
        max_trade_size=Decimal(10),
        trigger_min_size=1.0,
        trigger_imbalance_ratio=0.10,
        min_seconds_between_triggers=5.0,
        book_type="L1_MBP",
        use_quote_ticks=True,
    ),
)

Backtest setup

python
from nautilus_trader.backtest.config import BacktestEngineConfig
from nautilus_trader.backtest.engine import BacktestEngine
from nautilus_trader.config import LoggingConfig
from nautilus_trader.model.enums import AccountType
from nautilus_trader.model.enums import OmsType
from nautilus_trader.model.identifiers import TraderId
from nautilus_trader.model.identifiers import Venue
from nautilus_trader.model.objects import Money

engine = BacktestEngine(
    BacktestEngineConfig(
        trader_id=TraderId("BACKTESTER-001"),
        logging=LoggingConfig(log_level="INFO"),
    ),
)

AX = Venue("AX")
engine.add_venue(
    venue=AX,
    oms_type=OmsType.NETTING,
    account_type=AccountType.MARGIN,
    base_currency=USD,
    starting_balances=[Money(100_000, USD)],
)

engine.add_instrument(XAU_PERP)
engine.add_data(quotes)
engine.add_strategy(strategy)
engine.run()

Reports are on engine.trader:

python
print(engine.trader.generate_account_report(AX))
print(engine.trader.generate_order_fills_report())
print(engine.trader.generate_positions_report())

engine.reset()
engine.dispose()

The runnable example is at architect_ax_book_imbalance.py.

What the run produces

Replaying 2024-11-15 GC.v.0 mbp-1 (one trading day) through OrderBookImbalance(0.10, 1.0, 5s) prints 2,378 FOK fills net into 5 closed position cycles. Cumulative realised pnl ends at -4,170 USD: the strategy bleeds steadily across the day, mostly through spread cost on incremental FOK fills that add to existing positions.

Figure 1. GC.v.0 top of book around the cycle that opened with a short entry near 09:26 and exited near 09:31, then re-entered long until 09:35. Triangles are entries from flat, crosses are returns to flat, open circles are incremental FOK fills that grew the position.

Figure 2. smaller / larger BBO size ratio across all sampled top-of-book snapshots, with the 0.10 trigger threshold marked. The mass left of the threshold is the addressable trigger region.

Figure 3. Mid price (top) and best bid/ask size in contracts (bottom) across the trading day. Top-of-book sizes flicker between roughly two and fifty contracts; the mid traverses about a fifteen-dollar range.

Figure 4. Cumulative realised USD pnl across the five closed position cycles. The slope is consistently negative and the per-cycle pnl is dominated by spread.

Regenerate the panels

A self-contained renderer re-runs the backtest with a quote-sampling actor and writes PNGs to the asset directory using the nautilus_dark tearsheet theme.

bash
uv sync --extra visualization
GC_DBN=tests/test_data/local/Databento/gc_gold_quotes.dbn.zst \
    python3 docs/tutorials/assets/gold_book_imbalance_ax/render_panels.py

Next steps

  • Stricter trigger. Lower trigger_imbalance_ratio to 0.05 or raise trigger_min_size to 5 to require more conviction before firing.
  • Different sessions. Replay regular trading hours (RTH) only or roll through several days to see how the strategy behaves across regimes.
  • Other instruments. AX offers FX perpetuals (EURUSD-PERP, GBPUSD-PERP) and silver (XAG-PERP). The same proxy approach works with the corresponding CME futures.
  • Go live on the AX sandbox. See the AX Exchange integration guide once the backtest behaves.

Running live

The same OrderBookImbalance strategy runs live against AX Exchange. The launch script swaps the BacktestEngine for a TradingNode with the AX data and execution clients configured. See the live example: ax_book_imbalance.py.

For connection setup and API key configuration, see the AX Exchange integration guide.

Further reading