matitos
9c3bee87ac
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.
180 lines
3.7 KiB
Python
180 lines
3.7 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from pydantic import BaseModel, ConfigDict, Field
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class Candle(BaseModel):
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ts: int
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open: float
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high: float
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low: float
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close: float
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volume: float
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class ParamSpec(BaseModel):
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name: str
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type: Literal["int", "float", "bool", "str"]
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default: Any
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description: str = ""
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min: float | None = None
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max: float | None = None
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class StrategyOut(BaseModel):
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key: str
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name: str
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description: str
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params: list[ParamSpec]
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class PositionOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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strategy_key: str
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symbol: str
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side: str
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qty: float
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entry_price: float
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status: str
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stop_loss: float | None = None
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take_profit: float | None = None
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exit_price: float | None = None
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realized_pnl: float | None = None
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unrealized_pnl: float | None = None
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opened_at: datetime
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closed_at: datetime | None = None
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class OrderOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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position_id: int | None
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strategy_key: str
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symbol: str
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side: str
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qty: float
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price: float
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fee: float
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reason: str
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created_at: datetime
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class EventOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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level: str
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message: str
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strategy_key: str | None
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created_at: datetime
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class EquityPointOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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equity: float
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cash: float
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price: float
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created_at: datetime
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class StatusOut(BaseModel):
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running: bool
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mode: str
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exchange: str
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symbol: str
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timeframe: str
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strategy_key: str | None
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strategy_name: str | None
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params: dict[str, Any] = Field(default_factory=dict)
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price: float | None
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cash: float
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equity: float
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starting_balance: float
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realized_pnl: float
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unrealized_pnl: float
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total_pnl: float
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open_positions: int
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last_tick: datetime | None
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error: str | None = None
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class StartRequest(BaseModel):
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strategy_key: str
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symbol: str | None = None
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timeframe: str | None = None
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params: dict[str, Any] = Field(default_factory=dict)
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starting_balance: float | None = None
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class ManualOrderRequest(BaseModel):
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side: Literal["buy", "sell"]
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qty: float | None = None
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quote_amount: float | None = None
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reason: str = "manual"
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class ClosePositionRequest(BaseModel):
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reason: str = "manual"
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class BacktestRequest(BaseModel):
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strategy_key: str
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params: dict[str, Any] = Field(default_factory=dict)
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symbol: str | None = None
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timeframe: str | None = None
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limit: int = Field(default=500, ge=20, le=1000)
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starting_balance: float | None = None
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class BacktestTradeOut(BaseModel):
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side: str
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entry_ts: int
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exit_ts: int
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entry_price: float
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exit_price: float
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qty: float
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pnl: float
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return_pct: float
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reason: str
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class BacktestPointOut(BaseModel):
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ts: int
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equity: float
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buy_hold: float
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price: float
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class BacktestMetricsOut(BaseModel):
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initial_equity: float
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final_equity: float
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total_return_pct: float
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buy_hold_return_pct: float
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max_drawdown_pct: float
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num_trades: int
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win_rate_pct: float
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profit_factor: float | None
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avg_trade_pct: float
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best_trade_pct: float
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worst_trade_pct: float
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sharpe: float | None
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class BacktestResult(BaseModel):
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strategy_key: str
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strategy_name: str
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symbol: str
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timeframe: str
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starting_balance: float
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metrics: BacktestMetricsOut
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equity_curve: list[BacktestPointOut]
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trades: list[BacktestTradeOut]
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error: str | None = None
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