Composite Scanner

Meta-scanner that scores all arbitrage methods and routes capital to the best opportunities.

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Composite Arbitrage Scanner

A meta-scanner that runs all configured arbitrage methods, scores opportunities, and routes capital to the best ones. The ultimate arbitrage tool for running a full arb desk.

How It Works

1. Configure which arb methods to run with weights and capital limits
2. Each cycle, the composite scanner checks ctx.params for results from individual scanners
3. Score each opportunity: score = (net_edge / cost) * confidence * weight * (1 - utilization)
4. Rank and execute the top-N (limited by max_concurrent and total_capital)

ArbMethodConfig

method = hz.ArbMethodConfig(
    method="parity",          # Method name
    weight=1.0,               # Scoring weight
    max_capital=500.0,        # Capital limit for this method
    enabled=True,             # Enable/disable
    kwargs={"size": 50.0},    # Extra parameters
)

Field	Type	Default	Description
method	str	required	"parity", "cross_exchange", "event", "spread", "stat", "mm", "latency"
weight	float	1.0	Scoring weight multiplier
max_capital	float	500.0	Max capital for this method
enabled	bool	True	Whether to include in scans
kwargs	dict	{}	Extra parameters

Pipeline: composite_arb

scanner = hz.composite_arb(
    methods=[
        hz.ArbMethodConfig(method="parity", weight=2.0),
        hz.ArbMethodConfig(method="cross_exchange", weight=1.5),
        hz.ArbMethodConfig(method="event", weight=1.0),
        hz.ArbMethodConfig(method="spread", weight=0.8),
        hz.ArbMethodConfig(method="stat", weight=1.0),
        hz.ArbMethodConfig(method="latency", weight=2.0),
    ],
    total_capital=1000.0,
    max_concurrent=3,
    rebalance_interval=60.0,
    auto_execute=False,
    cooldown=5.0,
)

Parameter	Type	Default	Description
methods	list[ArbMethodConfig]	required	Methods to run
total_capital	float	1000.0	Total capital budget
max_concurrent	int	3	Max simultaneous positions
rebalance_interval	float	60.0	Seconds between rebalancing
auto_execute	bool	False	Auto-execute top opportunities
cooldown	float	5.0	Seconds between executions

Stores list[CompositeArbResult] in ctx.params["last_composite_arb"].

CompositeArbResult

Field	Type	Description
method	str	Which arb method found the opportunity
score	float	Composite score
net_edge	float	Expected edge
capital_needed	float	Capital required
details	dict	Method-specific details
timestamp	float	Detection timestamp

Example: Full Arb Desk

import horizon as hz

# Individual scanners feed results into ctx.params
parity = hz.parity_arb_scanner(
    "election-winner",
    exchange="polymarket",
    feed_name="polymarket",
    auto_execute=False,
)
cross = hz.arb_scanner(
    "election-winner",
    exchanges=["polymarket", "kalshi"],
    feed_map={"polymarket": "polymarket", "kalshi": "kalshi"},
)
spread = hz.spread_convergence(
    "gop-senate", "gop-house",
    "gop_senate_feed", "gop_house_feed",
)

# Composite scanner reads their results and decides what to execute
composite = hz.composite_arb(
    methods=[
        hz.ArbMethodConfig(method="parity", weight=2.0),
        hz.ArbMethodConfig(method="cross_exchange", weight=1.5),
        hz.ArbMethodConfig(method="spread", weight=0.8),
    ],
    total_capital=5000.0,
    max_concurrent=2,
    rebalance_interval=30.0,
    auto_execute=True,
)

hz.run(
    name="arb_desk",
    exchanges=[
        hz.Polymarket(private_key="0x..."),
        hz.Kalshi(api_key="..."),
    ],
    markets=["election-winner", "gop-senate", "gop-house"],
    feeds={
        "polymarket": hz.PolymarketBook("election-winner"),
        "kalshi": hz.KalshiBook("KXELECTION-25"),
        "gop_senate_feed": hz.PolymarketBook("gop-senate"),
        "gop_house_feed": hz.PolymarketBook("gop-house"),
    },
    pipeline=[parity, cross, spread, composite],
    interval=0.5,
)

The composite scanner reads results from individual scanners via ctx.params. Run individual scanners earlier in the pipeline so their results are available when the composite scanner runs.