"""Prometheus metrics for all Stonks Oracle pipeline stages.

Provides counters, histograms, and gauges covering:
- Ingestion: items fetched, new items, errors, adapter latency
- Parsing: documents parsed, quality scores, low-quality flags
- Extraction: attempts, successes, failures, latency, confidence, retries
- Aggregation: trend windows computed, signal counts, contradiction scores
- Lake publication: facts published per table, write latency
- Trading: orders submitted, rejected, filled, risk evaluations

Requirements: 12.1, 12.2
Design: Section 12 (Observability and Operations)
"""
from __future__ import annotations

from prometheus_client import Counter, Gauge, Histogram, Info

# ---------------------------------------------------------------------------
# Service info
# ---------------------------------------------------------------------------

SERVICE_INFO = Info("stonks_oracle", "Stonks Oracle service metadata")

# ---------------------------------------------------------------------------
# Ingestion metrics
# ---------------------------------------------------------------------------

INGESTION_JOBS_TOTAL = Counter(
    "stonks_ingestion_jobs_total",
    "Total ingestion jobs processed",
    ["source_type", "status"],
)

INGESTION_ITEMS_FETCHED = Counter(
    "stonks_ingestion_items_fetched_total",
    "Total items fetched from external sources",
    ["source_type"],
)

INGESTION_ITEMS_NEW = Counter(
    "stonks_ingestion_items_new_total",
    "New (non-duplicate) items ingested",
    ["source_type"],
)

INGESTION_ITEMS_DEDUPED = Counter(
    "stonks_ingestion_items_deduped_total",
    "Items skipped due to deduplication",
    ["source_type"],
)

INGESTION_ERRORS = Counter(
    "stonks_ingestion_errors_total",
    "Ingestion errors by source type",
    ["source_type"],
)

INGESTION_ADAPTER_DURATION = Histogram(
    "stonks_ingestion_adapter_duration_seconds",
    "Adapter fetch latency in seconds",
    ["source_type"],
    buckets=(0.1, 0.5, 1, 2, 5, 10, 30, 60),
)

# ---------------------------------------------------------------------------
# Parsing metrics
# ---------------------------------------------------------------------------

PARSE_JOBS_TOTAL = Counter(
    "stonks_parse_jobs_total",
    "Total parse jobs processed",
    ["status"],
)

PARSE_QUALITY_SCORE = Histogram(
    "stonks_parse_quality_score",
    "Distribution of parser quality scores",
    buckets=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0),
)

PARSE_LOW_QUALITY_TOTAL = Counter(
    "stonks_parse_low_quality_total",
    "Documents flagged as low quality by the parser",
)

PARSE_DURATION = Histogram(
    "stonks_parse_duration_seconds",
    "Parse job duration in seconds",
    buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10),
)

# ---------------------------------------------------------------------------
# Extraction metrics
# ---------------------------------------------------------------------------

EXTRACTION_JOBS_TOTAL = Counter(
    "stonks_extraction_jobs_total",
    "Total extraction jobs processed",
    ["status"],
)

EXTRACTION_ATTEMPTS = Counter(
    "stonks_extraction_attempts_total",
    "Total Ollama extraction attempts (including retries)",
)

EXTRACTION_RETRIES = Counter(
    "stonks_extraction_retries_total",
    "Extraction retry count",
)

EXTRACTION_DURATION = Histogram(
    "stonks_extraction_duration_seconds",
    "Extraction total duration in seconds",
    buckets=(1, 2, 5, 10, 20, 30, 60, 120),
)

EXTRACTION_CONFIDENCE = Histogram(
    "stonks_extraction_confidence",
    "Distribution of extraction confidence scores",
    buckets=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0),
)

EXTRACTION_VALIDATION_ERRORS = Counter(
    "stonks_extraction_validation_errors_total",
    "Total validation errors across extractions",
)

EXTRACTION_TOKEN_ESTIMATE = Counter(
    "stonks_extraction_tokens_total",
    "Estimated token usage",
    ["direction"],
)

# ---------------------------------------------------------------------------
# Aggregation metrics
# ---------------------------------------------------------------------------

AGGREGATION_WINDOWS_COMPUTED = Counter(
    "stonks_aggregation_windows_total",
    "Trend windows computed",
    ["window"],
)

AGGREGATION_SIGNALS_PROCESSED = Counter(
    "stonks_aggregation_signals_total",
    "Signals processed during aggregation",
    ["window"],
)

AGGREGATION_CONTRADICTION_SCORE = Histogram(
    "stonks_aggregation_contradiction_score",
    "Distribution of contradiction scores in trend windows",
    buckets=(0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0),
)

AGGREGATION_DURATION = Histogram(
    "stonks_aggregation_duration_seconds",
    "Aggregation job duration in seconds",
    ["window"],
    buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10),
)

# ---------------------------------------------------------------------------
# Recommendation metrics
# ---------------------------------------------------------------------------

RECOMMENDATION_GENERATED = Counter(
    "stonks_recommendations_total",
    "Recommendations generated",
    ["action", "mode"],
)

RECOMMENDATION_SUPPRESSED = Counter(
    "stonks_recommendations_suppressed_total",
    "Recommendations suppressed due to low data quality",
)

RECOMMENDATION_CONFIDENCE = Histogram(
    "stonks_recommendation_confidence",
    "Distribution of recommendation confidence scores",
    buckets=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0),
)

# ---------------------------------------------------------------------------
# Lake publication metrics
# ---------------------------------------------------------------------------

LAKE_FACTS_PUBLISHED = Counter(
    "stonks_lake_facts_published_total",
    "Analytical facts published to the lakehouse",
    ["table_name"],
)

LAKE_PUBLISH_DURATION = Histogram(
    "stonks_lake_publish_duration_seconds",
    "Lake publication write latency in seconds",
    ["table_name"],
    buckets=(0.01, 0.05, 0.1, 0.25, 0.5, 1, 2, 5),
)

LAKE_PUBLISH_ERRORS = Counter(
    "stonks_lake_publish_errors_total",
    "Lake publication errors",
    ["table_name"],
)

LAKE_PUBLISH_BYTES = Counter(
    "stonks_lake_publish_bytes_total",
    "Total bytes written to the lakehouse",
    ["table_name"],
)

# ---------------------------------------------------------------------------
# Trading / broker metrics
# ---------------------------------------------------------------------------

ORDERS_SUBMITTED = Counter(
    "stonks_orders_submitted_total",
    "Orders submitted to broker",
    ["side", "order_type", "mode"],
)

ORDERS_REJECTED = Counter(
    "stonks_orders_rejected_total",
    "Orders rejected before broker submission",
    ["reason_category"],
)

ORDERS_FILLED = Counter(
    "stonks_orders_filled_total",
    "Orders filled by broker",
    ["side"],
)

ORDERS_DUPLICATES_PREVENTED = Counter(
    "stonks_orders_duplicates_prevented_total",
    "Duplicate orders prevented by idempotency checks",
    ["detected_via"],
)

RISK_EVALUATIONS_TOTAL = Counter(
    "stonks_risk_evaluations_total",
    "Risk evaluations performed",
    ["result"],
)

RISK_CHECK_FAILURES = Counter(
    "stonks_risk_check_failures_total",
    "Individual risk check failures",
    ["check_name"],
)

POSITIONS_SYNCED = Counter(
    "stonks_positions_synced_total",
    "Position sync operations completed",
)

# ---------------------------------------------------------------------------
# Active gauges
# ---------------------------------------------------------------------------

ACTIVE_JOBS = Gauge(
    "stonks_active_jobs",
    "Currently processing jobs by stage",
    ["stage"],
)

# ---------------------------------------------------------------------------
# Alerting metrics
# ---------------------------------------------------------------------------

ALERTS_FIRED = Counter(
    "stonks_alerts_fired_total",
    "Total alerts fired by rule",
    ["rule", "severity"],
)

ALERTS_RESOLVED = Counter(
    "stonks_alerts_resolved_total",
    "Total alerts resolved by rule",
    ["rule"],
)

ALERT_CHECK_DURATION = Histogram(
    "stonks_alert_check_duration_seconds",
    "Duration of alert evaluation cycle",
    buckets=(0.01, 0.05, 0.1, 0.25, 0.5, 1, 2, 5),
)

ALERT_ACTIVE = Gauge(
    "stonks_alert_active",
    "Whether an alert rule is currently firing (1) or resolved (0)",
    ["rule"],
)

# ---------------------------------------------------------------------------
# Dead-letter queue metrics
# ---------------------------------------------------------------------------

DLQ_ITEMS_TOTAL = Counter(
    "stonks_dlq_items_total",
    "Jobs sent to dead-letter queues",
    ["queue"],
)

DLQ_REPLAYED_TOTAL = Counter(
    "stonks_dlq_replayed_total",
    "Jobs replayed from dead-letter queues",
    ["queue"],
)

DLQ_DEPTH = Gauge(
    "stonks_dlq_depth",
    "Current dead-letter queue depth",
    ["queue"],
)