coinhunter-cli/src/coinhunter/services/market_data.py

"""Market data fetching and metric computation for precheck."""

from __future__ import annotations

import os

import ccxt

from .data_utils import norm_symbol, to_float


def get_exchange():
    from ..runtime import load_env_file

    load_env_file()
    api_key = os.getenv("BINANCE_API_KEY")
    secret = os.getenv("BINANCE_API_SECRET")
    if not api_key or not secret:
        raise RuntimeError("Missing BINANCE_API_KEY or BINANCE_API_SECRET in ~/.hermes/.env")
    ex = ccxt.binance({
        "apiKey": api_key,
        "secret": secret,
        "options": {"defaultType": "spot"},
        "enableRateLimit": True,
    })
    ex.load_markets()
    return ex


def fetch_ohlcv_batch(ex, symbols: set, timeframe: str, limit: int):
    results = {}
    for sym in sorted(symbols):
        try:
            ohlcv = ex.fetch_ohlcv(sym, timeframe=timeframe, limit=limit)
            if ohlcv and len(ohlcv) >= 2:
                results[sym] = ohlcv
        except Exception:
            pass
    return results


def compute_ohlcv_metrics(ohlcv_1h, ohlcv_4h, current_price, volume_24h=None):
    metrics = {}
    if ohlcv_1h and len(ohlcv_1h) >= 2:
        closes = [c[4] for c in ohlcv_1h]
        volumes = [c[5] for c in ohlcv_1h]
        metrics["change_1h_pct"] = round((closes[-1] - closes[-2]) / closes[-2] * 100, 2) if closes[-2] != 0 else None
        if len(closes) >= 5:
            metrics["change_4h_pct"] = round((closes[-1] - closes[-5]) / closes[-5] * 100, 2) if closes[-5] != 0 else None
        recent_vol = sum(volumes[-4:]) / 4 if len(volumes) >= 4 else None
        metrics["volume_1h_avg"] = round(recent_vol, 2) if recent_vol else None
        highs = [c[2] for c in ohlcv_1h[-4:]]
        lows = [c[3] for c in ohlcv_1h[-4:]]
        metrics["high_4h"] = round(max(highs), 8) if highs else None
        metrics["low_4h"] = round(min(lows), 8) if lows else None

    if ohlcv_4h and len(ohlcv_4h) >= 2:
        closes_4h = [c[4] for c in ohlcv_4h]
        volumes_4h = [c[5] for c in ohlcv_4h]
        metrics["change_4h_pct_from_4h"] = round((closes_4h[-1] - closes_4h[-2]) / closes_4h[-2] * 100, 2) if closes_4h[-2] != 0 else None
        recent_vol_4h = sum(volumes_4h[-2:]) / 2 if len(volumes_4h) >= 2 else None
        metrics["volume_4h_avg"] = round(recent_vol_4h, 2) if recent_vol_4h else None
        highs_4h = [c[2] for c in ohlcv_4h]
        lows_4h = [c[3] for c in ohlcv_4h]
        metrics["high_24h_calc"] = round(max(highs_4h), 8) if highs_4h else None
        metrics["low_24h_calc"] = round(min(lows_4h), 8) if lows_4h else None
        if highs_4h and lows_4h:
            avg_price = sum(closes_4h) / len(closes_4h)
            metrics["volatility_4h_pct"] = round((max(highs_4h) - min(lows_4h)) / avg_price * 100, 2)

    if current_price:
        if metrics.get("high_4h"):
            metrics["distance_from_4h_high_pct"] = round((metrics["high_4h"] - current_price) / metrics["high_4h"] * 100, 2)
        if metrics.get("low_4h"):
            metrics["distance_from_4h_low_pct"] = round((current_price - metrics["low_4h"]) / metrics["low_4h"] * 100, 2)
        if metrics.get("high_24h_calc"):
            metrics["distance_from_24h_high_pct"] = round((metrics["high_24h_calc"] - current_price) / metrics["high_24h_calc"] * 100, 2)
        if metrics.get("low_24h_calc"):
            metrics["distance_from_24h_low_pct"] = round((current_price - metrics["low_24h_calc"]) / metrics["low_24h_calc"] * 100, 2)

    if volume_24h and volume_24h > 0 and metrics.get("volume_1h_avg"):
        daily_avg_1h = volume_24h / 24
        metrics["volume_1h_multiple"] = round(metrics["volume_1h_avg"] / daily_avg_1h, 2)
    if volume_24h and volume_24h > 0 and metrics.get("volume_4h_avg"):
        daily_avg_4h = volume_24h / 6
        metrics["volume_4h_multiple"] = round(metrics["volume_4h_avg"] / daily_avg_4h, 2)

    return metrics


def enrich_candidates_and_positions(global_candidates, candidate_layers, positions_view, tickers, ex):
    symbols = set()
    for c in global_candidates:
        symbols.add(c["symbol"])
    for p in positions_view:
        sym = p.get("symbol")
        if sym:
            sym_ccxt = norm_symbol(sym)
            symbols.add(sym_ccxt)

    ohlcv_1h = fetch_ohlcv_batch(ex, symbols, "1h", 24)
    ohlcv_4h = fetch_ohlcv_batch(ex, symbols, "4h", 12)

    def _apply(target_list):
        for item in target_list:
            sym = item.get("symbol")
            if not sym:
                continue
            sym_ccxt = norm_symbol(sym)
            v24h = to_float(tickers.get(sym_ccxt, {}).get("quoteVolume"))
            metrics = compute_ohlcv_metrics(
                ohlcv_1h.get(sym_ccxt),
                ohlcv_4h.get(sym_ccxt),
                item.get("price") or item.get("last_price"),
                volume_24h=v24h,
            )
            item["metrics"] = metrics

    _apply(global_candidates)
    for band_list in candidate_layers.values():
        _apply(band_list)
    _apply(positions_view)
    return global_candidates, candidate_layers, positions_view


def regime_from_pct(pct: float | None) -> str:
    if pct is None:
        return "unknown"
    if pct >= 2.0:
        return "bullish"
    if pct <= -2.0:
        return "bearish"
    return "neutral"