Initial commit: TradeMind – Krypto-Trading-Bot mit Lernmodus
Per Podman deploybarer Bot, der Käufe und Verkäufe simuliert ausführt und sich aus den Ergebnissen weiter antrainiert. Aufbau - Einheitliche Bar-Verarbeitung für paper, backtest und live; ausgetauscht werden nur Datenquelle und Broker. - Börsenanbindung über ccxt: rund 100 Börsen allein über exchange.id erreichbar. Zugangsdaten kommen über ENV-Platzhalter, der Live-Modus ist doppelt abgesichert. - Paper-Broker mit Gebühren, Slippage, Börsenpräzision und Volumengrenzen. - Online trainierte logistische Regression bewertet jedes Einstiegssignal. Sie lernt aus realen Trade-Ergebnissen, aus Shadow-Labels aller Kandidaten – auch der abgelehnten – und aus Hintergrund-Stichproben; beim Kaltstart wird sie aus der Kurshistorie vorgelernt. - Risikomanagement: Positions- und Exposure-Grenzen, ATR-Stops, Cooldown sowie Tagesverlust- und Drawdown-Notbremsen. - SQLite-Persistenz, HTTP-Status mit Prometheus-Metriken und Dashboard, Webhooks. Deployment - Containerfile (zweistufig, non-root UID 10001), podman-compose, systemd-Quadlet. - Modell und Datenbank liegen im Volume /data und überleben Neustarts. 128 Tests, ruff sauber. Verifiziert gegen echte Marktdaten sowie im gebauten Container inklusive Healthcheck und Zustandswiederherstellung.
This commit is contained in:
Tobias Zimmermann
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"""Konfiguration: YAML laden, ``${ENV}``-Platzhalter auflösen, per pydantic validieren."""
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from __future__ import annotations
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import os
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import re
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from enum import Enum
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from pathlib import Path
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from typing import Any
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import yaml
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from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
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_ENV_PATTERN = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)(?::-([^}]*))?\}")
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LIVE_CONFIRMATION_PHRASE = "I_UNDERSTAND_THE_RISK"
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class Mode(str, Enum):
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PAPER = "paper"
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LIVE = "live"
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BACKTEST = "backtest"
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class _Base(BaseModel):
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model_config = ConfigDict(extra="forbid")
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class ExchangeConfig(_Base):
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"""Anbindung an eine Börse. ``id`` ist eine beliebige ccxt-Exchange-ID."""
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id: str = "binance"
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api_key: str | None = None
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api_secret: str | None = None
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password: str | None = None # OKX, KuCoin, Coinbase Advanced ...
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uid: str | None = None
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sandbox: bool = True
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enable_rate_limit: bool = True
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timeout_ms: int = Field(default=20_000, ge=1_000)
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options: dict[str, Any] = Field(default_factory=lambda: {"defaultType": "spot"})
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@field_validator("id")
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@classmethod
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def _lower(cls, v: str) -> str:
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return v.strip().lower()
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def has_credentials(self) -> bool:
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return bool(self.api_key and self.api_secret)
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class MarketConfig(_Base):
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symbols: list[str] = Field(default_factory=lambda: ["BTC/USDT"])
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timeframe: str = "5m"
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history_bars: int = Field(default=500, ge=60, le=5_000)
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poll_interval_seconds: float = Field(default=20.0, gt=0)
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@field_validator("symbols")
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@classmethod
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def _non_empty(cls, v: list[str]) -> list[str]:
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if not v:
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raise ValueError("market.symbols darf nicht leer sein")
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return [s.strip().upper() for s in v]
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class PaperConfig(_Base):
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"""Parameter des simulierten Brokers."""
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starting_balance: float = Field(default=10_000.0, gt=0)
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quote_currency: str = "USDT"
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fee_rate: float = Field(default=0.001, ge=0, le=0.05)
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slippage_bps: float = Field(default=5.0, ge=0, le=500)
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# Teilausführungen bei zu großem Ordervolumen relativ zum Bar-Volumen
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max_volume_participation: float = Field(default=0.1, gt=0, le=1.0)
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class RiskConfig(_Base):
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max_position_pct: float = Field(default=0.2, gt=0, le=1.0)
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max_total_exposure_pct: float = Field(default=0.6, gt=0, le=1.0)
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max_open_positions: int = Field(default=3, ge=1)
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stop_loss_atr_mult: float = Field(default=2.0, ge=0)
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take_profit_atr_mult: float = Field(default=3.0, ge=0)
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trailing_stop_atr_mult: float = Field(default=0.0, ge=0)
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max_holding_bars: int = Field(default=0, ge=0) # 0 = unbegrenzt
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max_daily_loss_pct: float = Field(default=0.05, ge=0, le=1.0)
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max_drawdown_pct: float = Field(default=0.25, ge=0, le=1.0)
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min_notional: float = Field(default=10.0, ge=0)
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cooldown_bars_after_exit: int = Field(default=3, ge=0)
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class RuleConfig(_Base):
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fast_ema: int = Field(default=12, ge=2)
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slow_ema: int = Field(default=26, ge=3)
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rsi_period: int = Field(default=14, ge=2)
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rsi_oversold: float = Field(default=35.0, ge=1, le=99)
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rsi_overbought: float = Field(default=70.0, ge=1, le=99)
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atr_period: int = Field(default=14, ge=2)
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trend_filter_period: int = Field(default=100, ge=0) # 0 = aus
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# Mindesthaltedauer für signalbasierte Ausstiege. Verhindert, dass ein frischer
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# Einstieg sofort wieder ausgestoppt wird. Stop-Loss und Take-Profit gelten immer.
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min_holding_bars: int = Field(default=3, ge=0)
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@model_validator(mode="after")
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def _ema_order(self) -> RuleConfig:
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if self.fast_ema >= self.slow_ema:
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raise ValueError("strategy.rules.fast_ema muss kleiner als slow_ema sein")
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return self
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class LearnerConfig(_Base):
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"""Online-Lernen: Bewertung von Einstiegssignalen anhand realisierter Ergebnisse."""
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enabled: bool = True
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model_path: str = "/data/models/adaptive.npz"
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entry_threshold: float = Field(default=0.55, ge=0.0, le=1.0)
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exploration_rate: float = Field(default=0.05, ge=0.0, le=1.0)
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learning_rate: float = Field(default=0.02, gt=0)
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l2: float = Field(default=1e-4, ge=0)
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replay_size: int = Field(default=5_000, ge=100)
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batch_size: int = Field(default=64, ge=1)
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train_every_n_samples: int = Field(default=5, ge=1)
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warmup_samples: int = Field(default=200, ge=1)
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label_horizon_bars: int = Field(default=12, ge=1)
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label_target_bps: float = Field(default=30.0, ge=0)
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trade_sample_weight: float = Field(default=3.0, gt=0)
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# Einstiegssignale sind selten. Zusätzliche Stichproben des Marktzustands beschleunigen
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# die Aufwärmphase erheblich (0 = aus).
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background_sample_every_n_bars: int = Field(default=10, ge=0)
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background_sample_weight: float = Field(default=0.5, gt=0)
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# Beim Start ein noch untrainiertes Modell aus der Kurshistorie vorlernen, statt
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# tagelang auf genügend Live-Beobachtungen zu warten (0 = aus).
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bootstrap_bars: int = Field(default=3_000, ge=0)
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freeze_in_live: bool = False
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save_every_n_updates: int = Field(default=50, ge=1)
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class StrategyConfig(_Base):
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name: str = "adaptive" # adaptive | rules
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rules: RuleConfig = Field(default_factory=RuleConfig)
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learner: LearnerConfig = Field(default_factory=LearnerConfig)
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@field_validator("name")
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@classmethod
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def _known(cls, v: str) -> str:
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v = v.strip().lower()
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if v not in {"adaptive", "rules"}:
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raise ValueError("strategy.name muss 'adaptive' oder 'rules' sein")
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return v
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class StorageConfig(_Base):
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database_path: str = "/data/trademind.sqlite3"
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class ServerConfig(_Base):
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enabled: bool = True
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host: str = "0.0.0.0"
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port: int = Field(default=8080, ge=1, le=65535)
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enable_metrics: bool = True
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class NotificationConfig(_Base):
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webhook_url: str | None = None
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notify_on_trade: bool = True
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notify_on_risk_halt: bool = True
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class BacktestConfig(_Base):
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start: str | None = None # ISO-8601, z.B. 2024-01-01T00:00:00Z
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end: str | None = None
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bars: int = Field(default=5_000, ge=100)
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csv_dir: str | None = None # optional: OHLCV aus CSV statt von der Börse
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class Config(_Base):
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mode: Mode = Mode.PAPER
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log_level: str = "INFO"
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live_confirmation: str | None = None
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exchange: ExchangeConfig = Field(default_factory=ExchangeConfig)
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market: MarketConfig = Field(default_factory=MarketConfig)
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paper: PaperConfig = Field(default_factory=PaperConfig)
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risk: RiskConfig = Field(default_factory=RiskConfig)
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strategy: StrategyConfig = Field(default_factory=StrategyConfig)
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storage: StorageConfig = Field(default_factory=StorageConfig)
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server: ServerConfig = Field(default_factory=ServerConfig)
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notifications: NotificationConfig = Field(default_factory=NotificationConfig)
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backtest: BacktestConfig = Field(default_factory=BacktestConfig)
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@field_validator("log_level")
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@classmethod
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def _level(cls, v: str) -> str:
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v = v.strip().upper()
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if v not in {"DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"}:
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raise ValueError(f"Unbekannter log_level: {v}")
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return v
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@model_validator(mode="after")
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def _live_guard(self) -> Config:
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if self.mode is Mode.LIVE:
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if self.live_confirmation != LIVE_CONFIRMATION_PHRASE:
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raise ValueError(
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"Live-Modus erfordert 'live_confirmation: "
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f"{LIVE_CONFIRMATION_PHRASE}' in der Konfiguration."
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)
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if not self.exchange.has_credentials():
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raise ValueError("Live-Modus erfordert exchange.api_key und exchange.api_secret.")
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return self
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@property
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def is_simulated(self) -> bool:
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return self.mode in (Mode.PAPER, Mode.BACKTEST)
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def _substitute_env(node: Any) -> Any:
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"""Ersetzt ``${VAR}`` / ``${VAR:-default}`` rekursiv durch Umgebungsvariablen."""
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if isinstance(node, dict):
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return {k: _substitute_env(v) for k, v in node.items()}
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if isinstance(node, list):
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return [_substitute_env(v) for v in node]
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if not isinstance(node, str):
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return node
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def repl(match: re.Match[str]) -> str:
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name, default = match.group(1), match.group(2)
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return os.environ.get(name, default if default is not None else "")
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result = _ENV_PATTERN.sub(repl, node)
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# Ein Platzhalter, der zu einem leeren String auflöst, gilt als "nicht gesetzt".
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if result == "" and _ENV_PATTERN.search(node):
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return None
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return result
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_TRUE = {"1", "true", "yes", "on"}
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_FALSE = {"0", "false", "no", "off"}
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def _coerce(raw: str) -> Any:
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low = raw.strip().lower()
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if low in _TRUE:
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return True
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if low in _FALSE:
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return False
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try:
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return int(raw)
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except ValueError:
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pass
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try:
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return float(raw)
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except ValueError:
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pass
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if "," in raw:
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return [part.strip() for part in raw.split(",") if part.strip()]
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return raw
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def _apply_env_overrides(data: dict[str, Any], prefix: str = "TRADEMIND__") -> dict[str, Any]:
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"""``TRADEMIND__RISK__MAX_OPEN_POSITIONS=5`` überschreibt ``risk.max_open_positions``."""
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for key, value in os.environ.items():
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if not key.startswith(prefix) or not value:
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continue
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path = [part.lower() for part in key[len(prefix) :].split("__") if part]
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if not path:
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continue
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cursor: dict[str, Any] = data
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for part in path[:-1]:
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nxt = cursor.get(part)
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if not isinstance(nxt, dict):
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nxt = {}
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cursor[part] = nxt
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cursor = nxt
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cursor[path[-1]] = _coerce(value)
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return data
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def load_config(path: str | Path) -> Config:
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"""Lädt und validiert die Konfigurationsdatei."""
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p = Path(path)
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if not p.is_file():
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raise FileNotFoundError(f"Konfigurationsdatei nicht gefunden: {p}")
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raw = yaml.safe_load(p.read_text(encoding="utf-8")) or {}
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if not isinstance(raw, dict):
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raise ValueError(f"{p}: erwartet wurde ein YAML-Mapping auf oberster Ebene")
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data = _substitute_env(raw)
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data = _apply_env_overrides(data)
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return Config.model_validate(data)
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