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