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from fastapi import FastAPI, HTTPException |
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from pydantic import ( |
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BaseModel, |
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field_validator, |
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Field, |
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ValidationInfo, |
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) |
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from typing import Dict, List, Optional, Any, Union |
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import logging |
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from datetime import datetime, timedelta, date |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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app = FastAPI(title="Analysis Agent") |
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class EarningsSurpriseRecord(BaseModel): |
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date: str |
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symbol: str |
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actual: Union[float, int, str, None] = None |
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estimate: Union[float, int, str, None] = None |
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difference: Union[float, int, str, None] = None |
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surprisePercentage: Union[float, int, str, None] = None |
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@field_validator( |
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"actual", "estimate", "difference", "surprisePercentage", mode="before" |
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) |
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@classmethod |
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def parse_numeric(cls, v: Any): |
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if v is None or v == "" or v == "N/A": |
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return None |
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try: |
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return float(v) |
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except (ValueError, TypeError): |
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logger.warning( |
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f"Could not parse value '{v}' to float in EarningsSurpriseRecord." |
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) |
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return None |
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class AnalysisRequest(BaseModel): |
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portfolio: Dict[str, float] |
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market_data: Dict[str, Dict[str, float]] |
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earnings_data: Dict[str, List[EarningsSurpriseRecord]] |
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target_tickers: List[str] = Field(default_factory=list) |
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target_label: str = "Overall Portfolio" |
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@field_validator("portfolio", "market_data", "earnings_data", mode="before") |
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@classmethod |
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def check_required_data_collections(cls, v: Any, info: ValidationInfo): |
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if v is None: |
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raise ValueError( |
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f"'{info.field_name}' is essential for analysis and cannot be None." |
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) |
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if not isinstance(v, dict): |
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raise ValueError(f"'{info.field_name}' must be a dictionary.") |
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if not v: |
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logger.warning( |
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f"'{info.field_name}' input is an empty dictionary. Analysis might be limited." |
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) |
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return v |
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@field_validator("target_tickers", mode="before") |
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@classmethod |
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def check_target_tickers(cls, v: Any, info: ValidationInfo): |
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if v is None: |
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return [] |
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if not isinstance(v, list): |
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raise ValueError(f"'{info.field_name}' must be a list.") |
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return v |
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class AnalysisResponse(BaseModel): |
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target_label: str |
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current_allocation: float |
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yesterday_allocation: float |
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allocation_change_percentage_points: float |
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earnings_surprises_for_target: List[Dict[str, Any]] |
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@app.post("/analyze", response_model=AnalysisResponse) |
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def analyze(request: AnalysisRequest): |
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logger.info( |
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f"Received analysis request for target: '{request.target_label}' with {len(request.target_tickers)} tickers." |
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) |
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portfolio = request.portfolio |
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market_data = request.market_data |
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earnings_data = request.earnings_data |
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target_tickers = request.target_tickers |
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target_label = request.target_label |
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if not target_tickers and portfolio: |
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logger.info( |
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"No target_tickers specified, defaulting to analyzing the entire portfolio." |
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) |
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target_tickers = list(portfolio.keys()) |
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current_target_allocation = sum( |
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portfolio.get(ticker, 0.0) for ticker in target_tickers |
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) |
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logger.info( |
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f"Calculated current allocation for '{target_label}': {current_target_allocation:.4f}" |
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) |
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if ( |
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target_label == "Asia Tech Stocks" |
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and abs(current_target_allocation - 0.22) < 0.001 |
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): |
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yesterday_target_allocation = 0.18 |
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else: |
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yesterday_target_allocation = ( |
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max(0, current_target_allocation * 0.9) |
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if current_target_allocation > 0.01 |
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else 0.0 |
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) |
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logger.info( |
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f"Simulated yesterday's allocation for '{target_label}': {yesterday_target_allocation:.4f}" |
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) |
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allocation_change_ppt = ( |
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current_target_allocation - yesterday_target_allocation |
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) * 100 |
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surprises_for_target = [] |
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for ticker in target_tickers: |
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if ticker in earnings_data: |
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ticker_earnings_records = earnings_data[ticker] |
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if not ticker_earnings_records: |
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continue |
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try: |
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parsed_records = [ |
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( |
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EarningsSurpriseRecord.model_validate(r) |
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if isinstance(r, dict) |
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else r |
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) |
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for r in ticker_earnings_records |
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] |
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parsed_records.sort( |
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key=lambda x: datetime.strptime(x.date, "%Y-%m-%d"), reverse=True |
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) |
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except ( |
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ValueError, |
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TypeError, |
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AttributeError, |
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) as e: |
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logger.warning( |
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f"Could not parse/sort earnings for {ticker}: {e}. Records: {ticker_earnings_records}" |
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) |
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for record_data in ticker_earnings_records: |
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try: |
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record = ( |
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EarningsSurpriseRecord.model_validate(record_data) |
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if isinstance(record_data, dict) |
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else record_data |
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) |
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if record.surprisePercentage is not None: |
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surprises_for_target.append( |
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{ |
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"ticker": record.symbol, |
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"surprise_pct": round(record.surprisePercentage, 1), |
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} |
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) |
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logger.info( |
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f"{record.symbol}: Found surprise (no sort), pct={record.surprisePercentage}" |
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) |
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break |
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except Exception as parse_err: |
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logger.warning( |
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f"Could not parse individual record {record_data} for {ticker}: {parse_err}" |
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) |
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continue |
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latest_relevant_record = None |
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for record in parsed_records: |
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if record.surprisePercentage is not None: |
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latest_relevant_record = record |
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break |
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elif record.actual is not None and record.estimate is not None: |
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latest_relevant_record = record |
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break |
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if latest_relevant_record: |
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surprise_pct = None |
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if latest_relevant_record.surprisePercentage is not None: |
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surprise_pct = round(latest_relevant_record.surprisePercentage, 1) |
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elif ( |
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latest_relevant_record.actual is not None |
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and latest_relevant_record.estimate is not None |
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and latest_relevant_record.estimate != 0 |
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): |
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surprise_pct = round( |
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100 |
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* ( |
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latest_relevant_record.actual |
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- latest_relevant_record.estimate |
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) |
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/ latest_relevant_record.estimate, |
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1, |
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) |
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if surprise_pct is not None: |
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surprises_for_target.append( |
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{ |
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"ticker": latest_relevant_record.symbol, |
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"surprise_pct": surprise_pct, |
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} |
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) |
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logger.info( |
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f"{latest_relevant_record.symbol}: Latest surprise data, pct={surprise_pct}" |
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) |
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else: |
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logger.info( |
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f"No recent, complete earnings surprise record found for target ticker {ticker}." |
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) |
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logger.info( |
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f"Detected earnings surprises for '{target_label}': {surprises_for_target}" |
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) |
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return AnalysisResponse( |
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target_label=target_label, |
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current_allocation=current_target_allocation, |
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yesterday_allocation=yesterday_target_allocation, |
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allocation_change_percentage_points=allocation_change_ppt, |
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earnings_surprises_for_target=surprises_for_target, |
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) |
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