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from __future__ import annotations |
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from pydantic import BaseModel, Field |
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from typing import Literal, Optional |
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from datetime import datetime |
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class Summary(BaseModel): |
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major_concern_nifty50: str = Field(..., description="Single most important risk or watch-out for Nifty50") |
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trade_reasoning_nifty50: str |
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trade_strategy_nifty50: str |
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major_concern_banknifty: str |
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trade_reasoning_banknifty: str |
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trade_strategy_banknifty: str |
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class SummaryBankNifty(BaseModel): |
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major_concern: str |
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sentiment: Literal["bullish", "bearish"] |
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reasoning: str |
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trade_strategy: str |
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news_summary: str |
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class TradePlan(BaseModel): |
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status: Literal["No trade", "Trade"] |
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brief_reason: str |
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type: Literal["long", "short", "none"] |
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entry_at: float |
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target: float |
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stoploss: float |
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class DecisionOutput(BaseModel): |
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summary_banknifty: SummaryBankNifty |
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trade: TradePlan |
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class PositionState(BaseModel): |
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entered: bool = False |
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entry_time: Optional[datetime] = None |
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entry_price: Optional[float] = None |
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side: Optional[Literal["long", "short","none"]] = None |
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exited: bool = False |
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exit_time: Optional[datetime] = None |
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exit_price: Optional[float] = None |
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exit_reason: Optional[Literal["target", "stoploss", "Exited at market price","market close"]] = None |
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pnl_pct: Optional[float] = None |
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open_position: bool = False |
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unrealized_pct: Optional[float] = None |
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note: Optional[str] = None |
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def to_compact(self, now: datetime) -> dict: |
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keys = [ |
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"entered","entry_price","side","exited","exit_price","exit_reason", |
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"pnl_pct","open_position","unrealized_pct","note" |
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] |
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out = {k: getattr(self, k) for k in keys if getattr(self, k) is not None} |
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if self.open_position and self.entry_time: |
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out["trade_duration"] = now - self.entry_time |
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return out |
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TRADE_SCHEMA = { |
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"type": "object", |
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"properties": { |
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"trade": { |
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"type": "object", |
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"properties": { |
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"status": {"type": "string", "enum": ["No trade", "Trade"]}, |
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"brief_reason": {"type": "string"}, |
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"type": {"type": "string", "enum": ["long", "short", "none"]}, |
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"entry_at": {"type": "number"}, |
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"target": {"type": "number"}, |
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"stoploss": {"type": "number"} |
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}, |
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"required": ["status","brief_reason", "type", "entry_at", "target", "stoploss"], |
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"additionalProperties": False |
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} |
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}, |
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"required": ["trade"], |
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"additionalProperties": False |
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} |
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RESPONSE_SCHEMA = { |
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"type": "object", |
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"properties": { |
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"summary_banknifty": { |
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"type": "object", |
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"properties": { |
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"major_concern": {"type": "string"}, |
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"sentiment": {"type": "string", "enum": ["bullish", "bearish"]}, |
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"reasoning": {"type": "string"}, |
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"trade_strategy": {"type": "string"}, |
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"news_summary": {"type": "string"} |
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}, |
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"required": ["major_concern", "sentiment", "reasoning", "trade_strategy","news_summary"], |
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"additionalProperties": False |
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}, |
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"trade": { |
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"type": "object", |
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"properties": { |
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"status": {"type": "string", "enum": ["No trade", "Trade"]}, |
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"brief_reason": {"type": "string"}, |
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"type": {"type": "string", "enum": ["long", "short", "none"]}, |
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"entry_at": {"type": "number"}, |
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"target": {"type": "number"}, |
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"stoploss": {"type": "number"} |
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}, |
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"required": ["status", "brief_reason", "type", "entry_at", "target", "stoploss"], |
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"additionalProperties": False |
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} |
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}, |
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"required": ["summary_banknifty", "trade"], |
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"additionalProperties": False |
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} |
