c50843933f
New service: real-time market data collection with unified event model. Architecture: - Domain events: TradeEvent, QuoteEvent, BookL2Event, HeartbeatEvent - Provider interface: MarketDataProvider ABC with connect/subscribe/stream/close - Async EventBus with fan-out to multiple consumers Providers: - BinanceProvider: public WebSocket (trades + bookTicker), no API key needed, auto-reconnect with exponential backoff, heartbeat timeout detection - AlpacaProvider: IEX real-time data + paper trading auth, dry-run mode when no keys configured (heartbeats only) Consumers: - StorageConsumer: SQLite (via SQLAlchemy async) + JSONL append-only log - MetricsConsumer: Prometheus counters, latency histograms, events/sec gauge - PrintConsumer: sampled structured logging (1/100 events) CLI: python -m app run --provider binance --symbols BTCUSDT,ETHUSDT HTTP: /health, /metrics (Prometheus), /latest?symbol=XXX Tests: 19/19 passed (Binance parse, Alpaca parse, bus smoke tests) Config: pydantic-settings + .env, all secrets via environment variables. Co-authored-by: Cursor <cursoragent@cursor.com>
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
"""
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MetricsConsumer: Prometheus counters + latency histograms.
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"""
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from __future__ import annotations
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import logging
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import time
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from prometheus_client import Counter, Gauge, Histogram, Summary
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from app.domain.events import Event, EventType
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logger = logging.getLogger(__name__)
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# ── Prometheus metrics ─────────────────────────────────────────────────
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EVENTS_TOTAL = Counter(
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"market_events_total",
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"Total market data events received",
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["provider", "event_type", "symbol"],
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)
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EVENTS_PER_SECOND = Gauge(
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"market_events_per_second",
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"Approximate events per second",
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["provider"],
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)
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EXCHANGE_LATENCY = Histogram(
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"market_exchange_latency_ms",
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"Latency from exchange timestamp to receive (ms)",
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["provider"],
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buckets=[1, 5, 10, 25, 50, 100, 250, 500, 1000, 5000],
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)
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RECV_LATENCY = Summary(
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"market_recv_latency_ns",
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"Internal receive latency (nanoseconds, monotonic)",
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["provider"],
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)
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GAPS = Counter(
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"market_gaps_total",
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"Number of detected message gaps (heartbeat timeouts)",
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["provider"],
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)
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class MetricsConsumer:
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"""
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Computes and exposes Prometheus metrics from the event stream.
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"""
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def __init__(self) -> None:
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self._last_ts: dict[str, float] = {} # provider → last time.time()
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self._window_counts: dict[str, int] = {}
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self._window_start: dict[str, float] = {}
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async def handle(self, event: Event) -> None:
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provider = event.provider
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event_type = event.event_type.value
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symbol = getattr(event, "symbol", "__heartbeat__")
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# Count
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EVENTS_TOTAL.labels(
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provider=provider,
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event_type=event_type,
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symbol=symbol,
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).inc()
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# Exchange latency (if ts_exchange available)
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ts_exchange = getattr(event, "ts_exchange", None)
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if ts_exchange is not None:
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latency_ms = (event.ts_recv.timestamp() - ts_exchange.timestamp()) * 1000
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if 0 < latency_ms < 60_000: # sanity: 0–60s
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EXCHANGE_LATENCY.labels(provider=provider).observe(latency_ms)
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# Internal receive latency
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RECV_LATENCY.labels(provider=provider).observe(event.ts_recv_mono_ns)
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# Events/sec approximation (1-second window)
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now = time.time()
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if provider not in self._window_start:
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self._window_start[provider] = now
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self._window_counts[provider] = 0
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self._window_counts[provider] += 1
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elapsed = now - self._window_start[provider]
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if elapsed >= 1.0:
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eps = self._window_counts[provider] / elapsed
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EVENTS_PER_SECOND.labels(provider=provider).set(eps)
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self._window_start[provider] = now
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self._window_counts[provider] = 0
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# Gap detection: heartbeat events signal a potential gap
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if event.event_type == EventType.HEARTBEAT:
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GAPS.labels(provider=provider).inc()
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