Crypto trading bot: engine, brokers, backtester, dashboard, deploy

FastAPI backend with a single-strategy async engine, paper and Binance
brokers, pure-python indicators, a backtester that shares the engine's
portfolio math, and a Vue dashboard with a live WebSocket feed. Adds the
Gitea Actions CI/CD workflow that deploys to Traefik via docker_default.

backend/app/market/fake.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+from app.market.base import MarketData
+from app.schemas import Candle
+
+
+class FakeMarketData(MarketData):
+    """In-memory market data for tests and offline demos."""
+
+    def __init__(self, candles: Sequence[Candle] | None = None) -> None:
+        self.candles: list[Candle] = list(candles or [])
+
+    @classmethod
+    def from_closes(cls, closes: Sequence[float]) -> FakeMarketData:
+        candles = [
+            Candle(
+                ts=1_700_000_000_000 + i * 60_000,
+                open=close,
+                high=close,
+                low=close,
+                close=close,
+                volume=1.0,
+            )
+            for i, close in enumerate(closes)
+        ]
+        return cls(candles)
+
+    @classmethod
+    def sine_wave(cls, n: int = 200, amplitude: float = 100.0, period: int = 20) -> FakeMarketData:
+        base = 1000.0
+        closes = [base + amplitude * math.sin(2 * math.pi * i / period) for i in range(n)]
+        return cls.from_closes(closes)
+
+    def append(self, candle: Candle) -> None:
+        self.candles.append(candle)
+
+    async def fetch_candles(self, symbol: str, timeframe: str, limit: int) -> list[Candle]:
+        return self.candles[-limit:]
+
+    async def fetch_price(self, symbol: str) -> float:
+        if not self.candles:
+            raise ValueError("no candles available")
+        return self.candles[-1].close