Trademind_OpenCode/trademind/exchange.py

"""Austauschschicht: abstrakter Broker + ccxt-Implementierung + Mock.

Der Broker versorgt die Engine mit Kursen (für Simulation) und platziert echte
Orders (nur Live-Modus). Für Backtesting/Simulation ohne Netzwerk steht ein
OfflineMock bereit.
"""

from __future__ import annotations

import abc
import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Optional

import pandas as pd

from .config import ExchangeConfig

log = logging.getLogger("trademind.exchange")


@dataclass
class Quote:
    bid: float
    ask: float
    ts: str = ""


class Broker(abc.ABC):
    name: str = "abstract"

    @abc.abstractmethod
    def fetch_ohlcv(
        self, symbol: str, timeframe: str, limit: int, since: Optional[int] = None
    ) -> pd.DataFrame:
        """Liefert OHLCV-Candles als DataFrame mit open/high/low/close/volume."""

    @abc.abstractmethod
    def fetch_ticker(self, symbol: str) -> Quote:
        """Letztes Bid/Ask."""

    @abc.abstractmethod
    def create_market_order(
        self, symbol: str, side: str, amount: float
    ) -> Dict[str, Any]:
        """Platziert eine Markerorder. side = 'buy' | 'sell'."""

    @abc.abstractmethod
    def fetch_balance(self) -> Dict[str, float]:
        """Verfügbare Balancen (free)."""

    def close(self) -> None:  # pragma: no cover - optional
        pass


def build_exchange(cfg: ExchangeConfig) -> Broker:
    """Erzeugt aus der Konfiguration einen konkreten Broker (via ccxt)."""
    import ccxt  # lazy import für schnellere Tests ohne ccxt

    if cfg.name not in ccxt.exchanges:
        raise ValueError(f"ccxt kennt Exchange '{cfg.name}' nicht")

    klass = getattr(ccxt, cfg.name)
    params: Dict[str, Any] = {
        "apiKey": cfg.api_key,
        "secret": cfg.api_secret,
        "password": cfg.password,
        "enableRateLimit": True,
    }
    broker = klass(params)
    if cfg.sandbox:
        broker.set_sandbox_mode(True)
    broker.name = cfg.name
    return broker


def build_data_broker(name: str) -> Broker:
    """Erzeugt einen Broker nur für öffentliche Kursdaten (ohne Keys, ohne Sandbox).

    `fetch_ohlcv`/`fetch_ticker` sind öffentliche Endpunkte – ideal für den
    Paper-/Simulationsmodus, der echte Marktkurse nutzt, aber keine Orders platziert.
    """
    import ccxt

    if name not in ccxt.exchanges:
        raise ValueError(f"ccxt kennt Exchange '{name}' nicht")
    exchange = getattr(ccxt, name)({"enableRateLimit": True})
    return CcxtBroker(exchange, name)


class CcxtBroker(Broker):
    """Wrapper rund um eine ccxt-Exchange-Instanz."""

    def __init__(self, exchange: Any, name: str = "ccxt"):
        self._ex = exchange
        self.name = name

    def _symbol(self, symbol: str) -> str:
        return symbol if "/" in symbol else symbol

    def fetch_ohlcv(
        self, symbol: str, timeframe: str, limit: int, since: Optional[int] = None
    ) -> pd.DataFrame:
        raw = self._ex.fetch_ohlcv(self._symbol(symbol), timeframe, since=since, limit=limit)
        df = pd.DataFrame(raw, columns=["ts", "open", "high", "low", "close", "volume"])
        df["time"] = pd.to_datetime(df["ts"], unit="ms")
        return df[["time", "open", "high", "low", "close", "volume"]]

    def fetch_ticker(self, symbol: str) -> Quote:
        t = self._ex.fetch_ticker(self._symbol(symbol))
        return Quote(bid=float(t.get("bid") or t.get("last")),
                     ask=float(t.get("ask") or t.get("last")), ts=str(t.get("timestamp", "")))

    def create_market_order(self, symbol: str, side: str, amount: float) -> Dict[str, Any]:
        log.info("LIVE order: %s %s %.8f", side, symbol, amount)
        order = self._ex.create_order(self._symbol(symbol), "market", side, amount)
        return {"id": order.get("id"), "side": side, "amount": amount, "price": order.get("average")}

    def fetch_balance(self) -> Dict[str, float]:
        bal = self._ex.fetch_balance()
        return {k: float(v.get("free") or 0.0) for k, v in bal.items() if isinstance(v, dict)}

    def close(self) -> None:
        try:
            self._ex.close()
        except Exception:  # pragma: no cover
            pass


class MockBroker(Broker):
    """Erzeugt deterministische OHLCV-Daten, damit Simulation & Backtest offline laufen."""

    def __init__(
        self,
        name: str = "mock",
        seed: int = 7,
        start_price: float = 50_000.0,
        drift: float = 0.0002,
        vol: float = 0.02,
        quote: Optional[Quote] = None,
    ):
        import numpy as np

        self._seed = seed
        self._start = start_price
        self._drift = drift
        self._vol = vol
        self._quote = quote
        self.name = name

    def fetch_ohlcv(
        self, symbol: str, timeframe: str, limit: int, since: Optional[int] = None
    ) -> pd.DataFrame:
        import numpy as np

        rng = np.random.default_rng(self._seed * 1000 + limit)
        n = limit
        drift = self._drift
        vol = self._vol
        steps = drift + vol * rng.standard_normal(n)
        close = self._start * np.exp(np.cumsum(steps))
        open_ = np.roll(close, 1)
        open_[0] = self._start
        spread = np.abs(rng.standard_normal(n)) * self._vol * close * 0.5
        high = np.maximum(open_, close) + spread
        low = np.minimum(open_, close) - spread
        volume = np.abs(rng.standard_normal(n)).sum() * 10 + rng.uniform(1, 100, n)
        idx = pd.date_range(end=pd.Timestamp.utcnow().floor("h"), periods=n, freq="h")
        return pd.DataFrame(
            {"time": idx, "open": open_, "high": high, "low": low, "close": close, "volume": volume}
        )

    def fetch_ticker(self, symbol: str) -> Quote:
        if self._quote:
            return self._quote
        df = self.fetch_ohlcv(symbol, "1h", 1)
        last = float(df["close"].iloc[-1])
        return Quote(bid=last * 0.99999, ask=last * 1.00001)

    def create_market_order(self, symbol: str, side: str, amount: float) -> Dict[str, Any]:
        q = self.fetch_ticker(symbol)
        price = q.ask if side == "buy" else q.bid
        log.info("MOCK order: %s %s %.8f @ %.4f", side, symbol, amount, price)
        return {"id": "mock", "side": side, "amount": amount, "price": price}

    def fetch_balance(self) -> Dict[str, float]:
        return {}