d256b65d30
Stack: FastAPI + React/TypeScript + SQLite + GPT-4o Features: Radar géopolitique, Marchés, Régime Macro, Journal de Bord MTM, Rapport IA, Super Contexte (base de raisonnement évolutive), Boucle feedback IA. Deploy: Docker + docker-compose + nginx pour openfin.open-squared.tech Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
156 lines
3.3 KiB
Python
156 lines
3.3 KiB
Python
from pydantic import BaseModel
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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from enum import Enum
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class AssetClass(str, Enum):
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ENERGY = "energy"
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METALS = "metals"
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AGRICULTURE = "agriculture"
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EQUITIES = "equities"
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INDICES = "indices"
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FOREX = "forex"
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CRYPTO = "crypto"
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RATES = "rates"
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class RiskLevel(str, Enum):
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LOW = "low"
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MEDIUM = "medium"
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HIGH = "high"
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EXTREME = "extreme"
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class GeopoliticalCategory(str, Enum):
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MILITARY = "military"
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SANCTIONS = "sanctions"
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ELECTIONS = "elections"
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NATURAL_DISASTER = "natural_disaster"
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HEALTH_CRISIS = "health_crisis"
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RESOURCE_SCARCITY = "resource_scarcity"
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TRADE_WAR = "trade_war"
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ENERGY_CRISIS = "energy_crisis"
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POLITICAL_SPEECH = "political_speech"
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FINANCIAL_CRISIS = "financial_crisis"
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class GeoEvent(BaseModel):
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id: str
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title: str
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summary: str
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category: GeopoliticalCategory
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date: datetime
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source: str
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impact_score: float # -1.0 to 1.0
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asset_impacts: Dict[AssetClass, float] # impact per asset class
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tags: List[str]
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is_processed: bool = False
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class MarketQuote(BaseModel):
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symbol: str
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name: str
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price: float
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change: float
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change_pct: float
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volume: int
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iv: Optional[float] = None # implied volatility
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asset_class: AssetClass
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timestamp: datetime
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class OptionsContract(BaseModel):
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symbol: str
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underlying: str
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expiry: str
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strike: float
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option_type: str # call / put
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bid: float
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ask: float
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last: float
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volume: int
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open_interest: int
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iv: float
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delta: float
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gamma: float
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theta: float
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vega: float
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rho: float
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class TradeIdea(BaseModel):
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id: str
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title: str
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rationale: str
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asset_class: AssetClass
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underlying: str
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strategy: str # e.g. "Bull Call Spread", "Long Put", "Straddle"
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legs: List[Dict[str, Any]]
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max_loss: float
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max_gain: Optional[float]
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breakeven: List[float]
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horizon_days: int
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confidence: float # 0-100
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geo_trigger: Optional[str]
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risk_level: RiskLevel
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capital_required: float
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created_at: datetime
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class BacktestParams(BaseModel):
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start_date: str
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end_date: str
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strategy: str
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underlying: str
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geo_filters: Optional[List[GeopoliticalCategory]] = None
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capital: float = 1000.0
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class BacktestResult(BaseModel):
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params: BacktestParams
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trades: List[Dict[str, Any]]
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total_return: float
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win_rate: float
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max_drawdown: float
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sharpe_ratio: float
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profit_factor: float
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equity_curve: List[Dict[str, Any]]
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class EconomicEvent(BaseModel):
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id: str
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title: str
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country: str
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date: datetime
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importance: str # low / medium / high
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previous: Optional[str]
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forecast: Optional[str]
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actual: Optional[str]
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asset_impact: List[AssetClass]
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class PortfolioPosition(BaseModel):
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id: str
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trade_idea_id: Optional[str]
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symbol: str
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strategy: str
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entry_date: datetime
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expiry: str
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legs: List[Dict[str, Any]]
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capital_invested: float
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current_value: float
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pnl: float
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pnl_pct: float
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status: str # open / closed / expired
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class GeoPatternMatch(BaseModel):
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pattern_id: str
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description: str
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historical_date: datetime
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current_similarity: float
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historical_outcome: str
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suggested_trades: List[str]
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asset_class: AssetClass
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