"""
Geopolitical pattern engine.
Scores current events against historical templates and generates trade signals.
"""
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
import json


# ── Historical geopolitical pattern library ───────────────────────────────────
GEO_PATTERNS = [
    {
        "id": "P001",
        "name": "Middle East Military Escalation → Oil Spike",
        "description": "Armed conflict or threat in Gulf region triggers Brent/WTI crude spike +10-20% within 2-4 weeks",
        "triggers": ["military", "energy", "sanctions"],
        "keywords": ["Iran", "Israel", "Saudi", "Gulf", "Strait of Hormuz", "OPEC"],
        "historical_instances": [
            {"date": "2019-09-14", "event": "Attack on Saudi Aramco facilities", "brent_move": +14.6, "days": 2},
            {"date": "2020-01-03", "event": "Soleimani assassination", "brent_move": +4.4, "days": 1},
            {"date": "2022-02-24", "event": "Russia invades Ukraine", "brent_move": +28.0, "days": 10},
        ],
        "suggested_trades": [
            {"strategy": "Bull Call Spread", "underlying": "USO", "rationale": "Oil ETF call spread, limited risk"},
            {"strategy": "Long Call", "underlying": "CL=F", "rationale": "WTI crude direct exposure"},
        ],
        "asset_class": "energy",
        "expected_move_pct": 12.0,
        "probability": 0.65,
        "horizon_days": 30,
    },
    {
        "id": "P002",
        "name": "US Tariff Announcement → Agriculture Selloff",
        "description": "Trump/US tariff threats on China cause immediate selloff in soy, corn, wheat (retaliatory risk)",
        "triggers": ["trade_war", "political_speech"],
        "keywords": ["tariff", "China", "trade", "soybean", "agriculture", "import duty"],
        "historical_instances": [
            {"date": "2018-07-06", "event": "US-China trade war tariffs", "zs_move": -10.2, "days": 30},
            {"date": "2019-05-10", "event": "Trump tariff escalation tweet", "zs_move": -5.8, "days": 5},
            {"date": "2025-02-01", "event": "Trump 25% tariff on Canada/Mexico", "zw_move": -3.4, "days": 3},
        ],
        "suggested_trades": [
            {"strategy": "Bear Put Spread", "underlying": "SOYB", "rationale": "Downside hedge on soy ETF"},
            {"strategy": "Long Put", "underlying": "ZS=F", "rationale": "Soybean futures put"},
        ],
        "asset_class": "agriculture",
        "expected_move_pct": -8.0,
        "probability": 0.70,
        "horizon_days": 21,
    },
    {
        "id": "P003",
        "name": "Geopolitical Risk Flight → Gold Rally",
        "description": "Major geopolitical uncertainty drives safe-haven demand for gold +5-15%",
        "triggers": ["military", "health_crisis", "financial_crisis", "elections"],
        "keywords": ["nuclear", "war", "crisis", "uncertainty", "safe haven", "debt ceiling"],
        "historical_instances": [
            {"date": "2022-02-24", "event": "Ukraine invasion", "gc_move": +6.8, "days": 14},
            {"date": "2023-10-07", "event": "Hamas attack on Israel", "gc_move": +9.2, "days": 30},
            {"date": "2020-03-01", "event": "COVID-19 fear peak", "gc_move": +12.1, "days": 45},
        ],
        "suggested_trades": [
            {"strategy": "Long Call", "underlying": "GLD", "rationale": "Gold ETF call for safe-haven rally"},
            {"strategy": "Bull Call Spread", "underlying": "GC=F", "rationale": "Gold futures spread, capped risk"},
        ],
        "asset_class": "metals",
        "expected_move_pct": 7.5,
        "probability": 0.72,
        "horizon_days": 30,
    },
    {
        "id": "P004",
        "name": "Fed Hawkish Pivot → Dollar Surge / EM Currency Crash",
        "description": "Fed signals higher-for-longer rates → USD Index rallies, EUR/USD drops",
        "triggers": ["political_speech"],
        "keywords": ["Fed", "interest rate", "hike", "hawkish", "inflation", "FOMC", "Powell"],
        "historical_instances": [
            {"date": "2022-06-15", "event": "Fed 75bps hike", "dxy_move": +3.2, "days": 5},
            {"date": "2023-03-22", "event": "Fed signals further hikes", "eurusd_move": -1.8, "days": 7},
        ],
        "suggested_trades": [
            {"strategy": "Bear Put Spread", "underlying": "FXE", "rationale": "EUR/USD put spread"},
            {"strategy": "Long Call", "underlying": "UUP", "rationale": "Dollar index ETF call"},
        ],
        "asset_class": "forex",
        "expected_move_pct": 3.0,
        "probability": 0.68,
        "horizon_days": 14,
    },
    {
        "id": "P005",
        "name": "China Economic Slowdown → Copper/Metals Selloff",
        "description": "Weak Chinese PMI or stimulus disappointment drives copper lower (China = 50%+ of global demand)",
        "triggers": ["resource_scarcity", "trade_war"],
        "keywords": ["China", "PMI", "slowdown", "recession", "property", "Evergrande", "copper demand"],
        "historical_instances": [
            {"date": "2015-08-24", "event": "China Black Monday", "hg_move": -8.4, "days": 5},
            {"date": "2022-11-01", "event": "China PMI contraction", "hg_move": -5.2, "days": 10},
        ],
        "suggested_trades": [
            {"strategy": "Long Put", "underlying": "COPX", "rationale": "Copper miners ETF put"},
            {"strategy": "Bear Put Spread", "underlying": "HG=F", "rationale": "Copper futures spread"},
        ],
        "asset_class": "metals",
        "expected_move_pct": -6.5,
        "probability": 0.60,
        "horizon_days": 21,
    },
    {
        "id": "P006",
        "name": "Ukraine/Russia War Escalation → Wheat Spike + Defense Rally",
        "description": "New escalation in Russia-Ukraine conflict → wheat/fertilizer spike, defense stocks rally",
        "triggers": ["military", "resource_scarcity"],
        "keywords": ["Russia", "Ukraine", "Zelensky", "Kyiv", "grain corridor", "Black Sea", "NATO"],
        "historical_instances": [
            {"date": "2022-02-24", "event": "Full-scale invasion", "zw_move": +50.0, "days": 45},
            {"date": "2022-07-22", "event": "Grain deal collapse threat", "zw_move": +6.3, "days": 3},
            {"date": "2023-07-17", "event": "Russia exits grain deal", "zw_move": +8.5, "days": 2},
        ],
        "suggested_trades": [
            {"strategy": "Long Call", "underlying": "WEAT", "rationale": "Wheat ETF call on supply shock"},
            {"strategy": "Bull Call Spread", "underlying": "LMT", "rationale": "Lockheed defense stock spread"},
        ],
        "asset_class": "agriculture",
        "expected_move_pct": 15.0,
        "probability": 0.58,
        "horizon_days": 45,
    },
    {
        "id": "P007",
        "name": "Natural Gas Supply Disruption → NG Price Spike",
        "description": "Pipeline disruption, LNG strike, or extreme weather drives natural gas +20-40%",
        "triggers": ["energy", "natural_disaster", "military"],
        "keywords": ["pipeline", "LNG", "natural gas", "Nord Stream", "gas supply", "storage"],
        "historical_instances": [
            {"date": "2022-09-26", "event": "Nord Stream pipeline explosion", "ng_move": +18.0, "days": 5},
            {"date": "2021-02-10", "event": "Texas winter storm Uri", "ng_move": +40.0, "days": 3},
        ],
        "suggested_trades": [
            {"strategy": "Long Call", "underlying": "UNG", "rationale": "Natural gas ETF call"},
            {"strategy": "Bull Call Spread", "underlying": "NG=F", "rationale": "NG futures spread, capped risk"},
        ],
        "asset_class": "energy",
        "expected_move_pct": 25.0,
        "probability": 0.55,
        "horizon_days": 14,
    },
    {
        "id": "P008",
        "name": "Pandemic / Health Crisis → VIX Spike + Market Selloff",
        "description": "New pandemic scare or major health crisis → VIX spike, equity selloff, gold bid",
        "triggers": ["health_crisis"],
        "keywords": ["pandemic", "virus", "outbreak", "WHO", "lockdown", "COVID", "mpox", "H5N1"],
        "historical_instances": [
            {"date": "2020-02-24", "event": "COVID-19 global spread fear", "spx_move": -34.0, "days": 30},
            {"date": "2022-11-25", "event": "China COVID lockdowns", "spx_move": -3.5, "days": 3},
        ],
        "suggested_trades": [
            {"strategy": "Long Put", "underlying": "SPY", "rationale": "S&P 500 put for equity protection"},
            {"strategy": "Long Call", "underlying": "^VIX", "rationale": "VIX call for volatility spike"},
            {"strategy": "Long Call", "underlying": "GLD", "rationale": "Gold safe-haven call"},
        ],
        "asset_class": "indices",
        "expected_move_pct": -12.0,
        "probability": 0.45,
        "horizon_days": 30,
    },
]


GEOPOLITICAL_RISK_WEIGHTS = {
    "military": 0.25,
    "energy": 0.20,
    "trade_war": 0.15,
    "political_speech": 0.15,
    "natural_disaster": 0.10,
    "health_crisis": 0.10,
    "resource_scarcity": 0.05,
}


def compute_geo_risk_score(events: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Compute a global geopolitical risk score 0-100 from recent events."""
    if not events:
        return {"score": 35, "level": "medium", "breakdown": {}}

    category_scores: Dict[str, float] = {}
    for event in events[:30]:
        cat = event.get("category", "general")
        impact = event.get("impact_score", 0.1)
        if cat in category_scores:
            category_scores[cat] = max(category_scores[cat], impact)
        else:
            category_scores[cat] = impact

    weighted = sum(
        category_scores.get(cat, 0) * weight
        for cat, weight in GEOPOLITICAL_RISK_WEIGHTS.items()
    )
    score = min(100, round(weighted * 100, 1))

    if score < 25:
        level = "low"
    elif score < 50:
        level = "medium"
    elif score < 75:
        level = "high"
    else:
        level = "extreme"

    return {
        "score": score,
        "level": level,
        "breakdown": {cat: round(v * 100, 1) for cat, v in category_scores.items()},
        "top_risks": sorted(category_scores.items(), key=lambda x: x[1], reverse=True)[:3],
    }


def match_patterns(events: List[Dict[str, Any]], patterns: Optional[List[Dict[str, Any]]] = None) -> List[Dict[str, Any]]:
    """Find which historical geo-patterns best match current event feed."""
    if not events:
        return []
    if patterns is None:
        patterns = GEO_PATTERNS

    current_categories = set(e.get("category", "") for e in events)
    current_tags = set()
    for e in events:
        current_tags.update(e.get("tags", []))
    current_text = " ".join(e.get("title", "") + " " + e.get("summary", "") for e in events[:20]).lower()

    matches = []
    for pattern in patterns:
        trigger_match = len(set(pattern["triggers"]) & current_categories) / len(pattern["triggers"])
        keyword_match = sum(1 for kw in pattern["keywords"] if kw.lower() in current_text) / len(pattern["keywords"])
        similarity = round((trigger_match * 0.5 + keyword_match * 0.5) * 100, 1)

        if similarity > 10:
            matches.append({
                "pattern_id": pattern["id"],
                "name": pattern["name"],
                "description": pattern["description"],
                "similarity": similarity,
                "suggested_trades": pattern["suggested_trades"],
                "asset_class": pattern["asset_class"],
                "expected_move_pct": pattern["expected_move_pct"],
                "probability": pattern["probability"],
                "horizon_days": pattern["horizon_days"],
                "historical_instances": pattern["historical_instances"],
            })

    return sorted(matches, key=lambda x: x["similarity"], reverse=True)[:5]


def generate_trade_ideas(pattern_matches: List[Dict[str, Any]], geo_score: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Convert pattern matches into structured trade ideas with sizing for ~1000€."""
    ideas = []
    for pm in pattern_matches[:5]:
        for i, trade in enumerate(pm["suggested_trades"]):  # all suggested trades, not just first
            move = pm["expected_move_pct"]
            confidence = round(pm["probability"] * pm["similarity"] / 100 * 100)
            # Use trade-level asset_class if provided, else fall back to pattern-level
            asset_class = trade.get("asset_class") or pm["asset_class"]
            ideas.append({
                "id": f"IDEA-{pm['pattern_id']}-{i}-{trade['strategy'][:3].upper()}",
                "title": f"{trade['strategy']} on {trade['underlying']}",
                "rationale": f"[{pm['name']}] {trade['rationale']}. Expected move: {'+' if move > 0 else ''}{move}% in {pm['horizon_days']}d",
                "pattern": pm["name"],
                "asset_class": asset_class,
                "underlying": trade["underlying"],
                "strategy": trade["strategy"],
                "expected_move_pct": move,
                "confidence": min(95, confidence),
                "horizon_days": pm["horizon_days"],
                "capital_required": 1000,
                "risk_level": "high" if abs(move) > 15 else "medium",
                "pattern_similarity": pm["similarity"],
            })
    return ideas


def compute_pattern_relevance(
    events: List[Dict[str, Any]],
    patterns: Optional[List[Dict[str, Any]]] = None,
) -> List[Dict[str, Any]]:
    """Return ALL patterns with news-keyword relevance score + matching news snippets.
    Unlike match_patterns(), no similarity threshold — every active pattern is returned.
    """
    if patterns is None:
        patterns = GEO_PATTERNS

    current_categories = set(e.get("category", "") for e in events)
    current_text = " ".join(
        e.get("title", "") + " " + e.get("summary", "") for e in events[:30]
    ).lower()

    result = []
    for pattern in patterns:
        triggers_list = pattern.get("triggers", []) or []
        keywords_list = pattern.get("keywords", []) or []

        trigger_match = (
            len(set(triggers_list) & current_categories) / len(triggers_list)
            if triggers_list else 0
        )
        kw_hits = [kw for kw in keywords_list if kw.lower() in current_text]
        keyword_match = len(kw_hits) / len(keywords_list) if keywords_list else 0
        relevance = round((trigger_match * 0.5 + keyword_match * 0.5) * 100, 1)

        # Find matching news with which keywords triggered
        matching_news = []
        for e in events[:30]:
            text = (e.get("title", "") + " " + e.get("summary", "")).lower()
            hits = [kw for kw in keywords_list if kw.lower() in text]
            if hits:
                matching_news.append({
                    "title": e.get("title", ""),
                    "source": e.get("source", ""),
                    "date": str(e.get("date", ""))[:16],
                    "impact": round(e.get("impact_score", 0), 2),
                    "matched_keywords": hits,
                    "url": e.get("url", ""),
                })
        matching_news.sort(key=lambda x: x["impact"], reverse=True)

        result.append({
            "pattern_id": pattern.get("id", ""),
            "name": pattern.get("name", ""),
            "description": pattern.get("description", ""),
            "asset_class": pattern.get("asset_class", ""),
            "relevance": relevance,
            "keyword_hits": len(kw_hits),
            "keyword_total": len(keywords_list),
            "matched_keywords": kw_hits,
            "matching_news": matching_news[:5],
            "suggested_trades": pattern.get("suggested_trades", []),
            "expected_move_pct": pattern.get("expected_move_pct", 0),
            "probability": pattern.get("probability", 0),
            "horizon_days": pattern.get("horizon_days", 0),
        })

    result.sort(key=lambda x: x["relevance"], reverse=True)
    return result


def get_all_patterns() -> List[Dict[str, Any]]:
    return GEO_PATTERNS