Phase 3: Complete govmap package extraction (step 2/3)
- Created market_analysis.py with market analysis functions: - parse_deal_dates() - Date parsing and filtering helper - calculate_market_activity_score() - Activity metrics - analyze_investment_potential() - Investment analysis - get_market_liquidity() - Liquidity metrics - Created client.py with GovmapClient class: - Core API methods (autocomplete, get deals, etc.) - Validation methods delegating to validators module - Utility methods delegating to utils module - Filtering, statistics, and analysis methods delegating to respective modules - Updated govmap/__init__.py to export GovmapClient All modules maintain backward compatibility through delegation pattern. Next: Delete old govmap.py and update imports. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Nitzan Pomerantz
@@ -0,0 +1,66 @@
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"""
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Govmap package - Israeli government real estate data API client.
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This package provides a modular interface to the Govmap API for querying
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Israeli real estate deals, market trends, and property information.
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Public API:
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- GovmapClient: Main API client class (to be added from client.py)
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- filter_deals_by_criteria: Filter deals by various criteria
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- calculate_deal_statistics: Calculate statistical aggregations
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- calculate_market_activity_score: Market activity and trend metrics
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- analyze_investment_potential: Investment analysis and price trends
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- get_market_liquidity: Market liquidity and velocity metrics
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"""
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# Filter functions
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from .filters import filter_deals_by_criteria
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# Statistics functions
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from .statistics import calculate_deal_statistics, calculate_std_dev
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# Market analysis functions
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from .market_analysis import (
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calculate_market_activity_score,
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analyze_investment_potential,
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get_market_liquidity,
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parse_deal_dates,
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)
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# Utility functions
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from .utils import calculate_distance, is_same_building, extract_floor_number
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# Validation functions
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from .validators import (
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validate_address,
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validate_coordinates,
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validate_positive_int,
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validate_deal_type,
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)
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# Main API client
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from .client import GovmapClient
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__all__ = [
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# Main client class
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"GovmapClient",
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# Filtering
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"filter_deals_by_criteria",
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# Statistics
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"calculate_deal_statistics",
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"calculate_std_dev",
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# Market analysis
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"calculate_market_activity_score",
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"analyze_investment_potential",
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"get_market_liquidity",
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"parse_deal_dates",
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# Utilities
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"calculate_distance",
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"is_same_building",
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"extract_floor_number",
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# Validation
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"validate_address",
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"validate_coordinates",
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"validate_positive_int",
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"validate_deal_type",
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]
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@@ -0,0 +1,740 @@
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"""
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Govmap API Client for Israeli real estate data.
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This module provides the main GovmapClient class for interacting with the
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Israeli government's Govmap API to retrieve property deals, market trends,
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and real estate information.
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"""
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import logging
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import time
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from typing import Any, Dict, List, Optional, Tuple
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from datetime import datetime, timedelta
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import requests
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from nadlan_mcp.config import GovmapConfig, get_config
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# Import functions from modular package
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from . import validators
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from . import utils
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from . import filters
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from . import statistics
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from . import market_analysis
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logger = logging.getLogger(__name__)
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class GovmapClient:
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"""
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A client for interacting with the Israeli government's Govmap API.
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This class provides methods to search for properties, find block/parcel information,
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and retrieve real estate deal data with automatic retries and rate limiting.
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Attributes:
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config: Configuration object with API settings
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session: Requests session for connection pooling
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last_request_time: Timestamp of last API request for rate limiting
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"""
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def __init__(self, config: Optional[GovmapConfig] = None):
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"""
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Initialize the GovmapClient.
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Args:
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config: Optional configuration object. If None, uses global config.
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"""
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self.config = config or get_config()
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self.base_url = self.config.base_url.rstrip("/")
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self.session = requests.Session()
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self.session.headers.update(
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{"Content-Type": "application/json", "User-Agent": self.config.user_agent}
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)
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self.last_request_time = 0.0
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def _rate_limit(self):
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"""
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Enforce rate limiting by sleeping if necessary.
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Ensures requests don't exceed the configured requests_per_second.
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"""
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min_interval = 1.0 / self.config.requests_per_second
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elapsed = time.time() - self.last_request_time
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if elapsed < min_interval:
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time.sleep(min_interval - elapsed)
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self.last_request_time = time.time()
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# Validation methods (delegate to validators module)
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def _validate_address(self, address: str) -> str:
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"""Validate and sanitize address input."""
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return validators.validate_address(address)
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def _validate_coordinates(self, point: Tuple[float, float]) -> Tuple[float, float]:
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"""Validate coordinate input."""
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return validators.validate_coordinates(point)
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def _validate_positive_int(
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self, value: int, name: str, max_value: Optional[int] = None
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) -> int:
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"""Validate positive integer input."""
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return validators.validate_positive_int(value, name, max_value)
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# Utility methods (delegate to utils module)
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def _calculate_distance(
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self, point1: Tuple[float, float], point2: Tuple[float, float]
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) -> float:
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"""Calculate Euclidean distance between two points in ITM coordinates."""
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return utils.calculate_distance(point1, point2)
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def _is_same_building(self, search_address: str, deal_address: str) -> bool:
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"""Check if a deal is from the same building as the search address."""
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return utils.is_same_building(search_address, deal_address)
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def _extract_floor_number(self, floor_str: str) -> Optional[int]:
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"""Extract numeric floor number from Hebrew floor description."""
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return utils.extract_floor_number(floor_str)
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# Core API methods
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def autocomplete_address(self, search_text: str) -> Dict[str, Any]:
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"""
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Find the most likely match for a given address using autocomplete.
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Args:
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search_text: The address to search for (e.g., "סוקולוב 38 חולון")
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Returns:
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Dict containing the JSON response from the API with coordinates
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Raises:
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requests.RequestException: If the API request fails after retries
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ValueError: If the response is invalid or input is invalid
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"""
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search_text = self._validate_address(search_text)
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url = f"{self.base_url}/search-service/autocomplete"
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payload = {
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"searchText": search_text,
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"language": "he",
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"isAccurate": False,
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"maxResults": 10,
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}
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# Retry logic with exponential backoff
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for attempt in range(self.config.max_retries + 1):
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try:
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self._rate_limit()
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logger.info(
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f"Searching for address: {search_text} (attempt {attempt + 1}/{self.config.max_retries + 1})"
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)
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timeout = (self.config.connect_timeout, self.config.read_timeout)
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response = self.session.post(url, json=payload, timeout=timeout)
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response.raise_for_status()
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data = response.json()
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if not data or "results" not in data:
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raise ValueError("Invalid response format from autocomplete API")
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return data
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except (requests.RequestException, requests.Timeout) as e:
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if attempt < self.config.max_retries:
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wait_time = min(
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self.config.retry_min_wait * (2**attempt),
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self.config.retry_max_wait,
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)
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logger.warning(
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f"Request failed (attempt {attempt + 1}), retrying in {wait_time}s: {e}"
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)
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time.sleep(wait_time)
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else:
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logger.error(
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f"Request failed after {self.config.max_retries + 1} attempts: {e}"
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)
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raise
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# This line should never be reached but satisfies type checker
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raise RuntimeError(
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"Unexpected error: retry loop exited without return or raise"
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)
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def get_gush_helka(self, point: Tuple[float, float]) -> Dict[str, Any]:
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"""
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Get Gush (Block) and Helka (Parcel) information for a coordinate point.
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Args:
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point: A tuple of (longitude, latitude)
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Returns:
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Dict containing the JSON response with block and parcel data
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Raises:
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requests.RequestException: If the API request fails after retries
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ValueError: If the response or input is invalid
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"""
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point = self._validate_coordinates(point)
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url = f"{self.base_url}/layers-catalog/entitiesByPoint"
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payload = {"point": list(point), "layers": [{"layerId": "16"}], "tolerance": 0}
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# Retry logic with exponential backoff
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for attempt in range(self.config.max_retries + 1):
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try:
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self._rate_limit()
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logger.info(
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f"Getting Gush/Helka for point: {point} (attempt {attempt + 1}/{self.config.max_retries + 1})"
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)
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timeout = (self.config.connect_timeout, self.config.read_timeout)
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response = self.session.post(url, json=payload, timeout=timeout)
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response.raise_for_status()
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data = response.json()
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return data
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except (requests.RequestException, requests.Timeout) as e:
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if attempt < self.config.max_retries:
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wait_time = min(
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self.config.retry_min_wait * (2**attempt),
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self.config.retry_max_wait,
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)
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logger.warning(
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f"Request failed (attempt {attempt + 1}), retrying in {wait_time}s: {e}"
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)
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time.sleep(wait_time)
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else:
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logger.error(
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f"Request failed after {self.config.max_retries + 1} attempts: {e}"
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)
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raise
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# This line should never be reached but satisfies type checker
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raise RuntimeError(
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"Unexpected error: retry loop exited without return or raise"
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)
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def get_deals_by_radius(
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self, point: Tuple[float, float], radius: int = 50
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) -> List[Dict[str, Any]]:
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"""
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Find real estate deals within a specified radius of a point.
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Args:
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point: A tuple of (longitude, latitude)
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radius: The search radius in meters (default: 50)
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Returns:
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List of deals found within the radius
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Raises:
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requests.RequestException: If the API request fails after retries
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ValueError: If the response or input is invalid
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"""
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point = self._validate_coordinates(point)
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radius = self._validate_positive_int(radius, "radius", max_value=5000)
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url = f"{self.base_url}/real-estate/deals/{point[0]},{point[1]}/{radius}"
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# Retry logic with exponential backoff
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for attempt in range(self.config.max_retries + 1):
|
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try:
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self._rate_limit()
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logger.info(
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f"Getting deals by radius for point: {point}, radius: {radius}m (attempt {attempt + 1}/{self.config.max_retries + 1})"
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)
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timeout = (self.config.connect_timeout, self.config.read_timeout)
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response = self.session.get(url, timeout=timeout)
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response.raise_for_status()
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data = response.json()
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if not isinstance(data, list):
|
||||
raise ValueError(
|
||||
f"Expected list response, got {type(data).__name__}"
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||||
)
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||||
return data
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||||
|
||||
except (requests.RequestException, requests.Timeout) as e:
|
||||
if attempt < self.config.max_retries:
|
||||
wait_time = min(
|
||||
self.config.retry_min_wait * (2**attempt),
|
||||
self.config.retry_max_wait,
|
||||
)
|
||||
logger.warning(
|
||||
f"Request failed (attempt {attempt + 1}), retrying in {wait_time}s: {e}"
|
||||
)
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(
|
||||
f"Request failed after {self.config.max_retries + 1} attempts: {e}"
|
||||
)
|
||||
raise
|
||||
# This line should never be reached but satisfies type checker
|
||||
raise RuntimeError(
|
||||
"Unexpected error: retry loop exited without return or raise"
|
||||
)
|
||||
|
||||
def get_street_deals(
|
||||
self,
|
||||
polygon_id: str,
|
||||
limit: int = 10,
|
||||
start_date: Optional[str] = None,
|
||||
end_date: Optional[str] = None,
|
||||
deal_type: int = 2,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Retrieve detailed information about deals on a specific street.
|
||||
|
||||
Args:
|
||||
polygon_id: The ID of the lot's polygon
|
||||
limit: Maximum number of deals to return (default: 10)
|
||||
start_date: Start date for search in 'YYYY-MM' format
|
||||
end_date: End date for search in 'YYYY-MM' format
|
||||
deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
|
||||
|
||||
Returns:
|
||||
List of detailed deal information for the street
|
||||
|
||||
Raises:
|
||||
requests.RequestException: If the API request fails after retries
|
||||
ValueError: If the response or input is invalid
|
||||
"""
|
||||
if not polygon_id or not isinstance(polygon_id, str):
|
||||
raise ValueError("polygon_id must be a non-empty string")
|
||||
polygon_id = polygon_id.strip()
|
||||
if not polygon_id:
|
||||
raise ValueError("polygon_id cannot be empty or whitespace only")
|
||||
|
||||
limit = self._validate_positive_int(limit, "limit", max_value=1000)
|
||||
validators.validate_deal_type(deal_type)
|
||||
|
||||
url = f"{self.base_url}/real-estate/street-deals/{polygon_id}"
|
||||
|
||||
params: Dict[str, Any] = {"limit": limit, "dealType": deal_type}
|
||||
if start_date:
|
||||
params["startDate"] = start_date
|
||||
if end_date:
|
||||
params["endDate"] = end_date
|
||||
|
||||
# Retry logic with exponential backoff
|
||||
for attempt in range(self.config.max_retries + 1):
|
||||
try:
|
||||
self._rate_limit()
|
||||
|
||||
logger.info(
|
||||
f"Getting street deals for polygon: {polygon_id}, dealType: {deal_type} (attempt {attempt + 1}/{self.config.max_retries + 1})"
|
||||
)
|
||||
timeout = (self.config.connect_timeout, self.config.read_timeout)
|
||||
response = self.session.get(url, params=params, timeout=timeout)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
# API returns {data: [...], totalCount: ..., limit: ..., offset: ...}
|
||||
if isinstance(data, dict) and "data" in data:
|
||||
if not isinstance(data["data"], list):
|
||||
raise ValueError(
|
||||
f"Expected list in 'data' field, got {type(data['data']).__name__}"
|
||||
)
|
||||
return data["data"]
|
||||
elif isinstance(data, list):
|
||||
return data
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unexpected response format: {type(data).__name__}"
|
||||
)
|
||||
|
||||
except (requests.RequestException, requests.Timeout) as e:
|
||||
if attempt < self.config.max_retries:
|
||||
wait_time = min(
|
||||
self.config.retry_min_wait * (2**attempt),
|
||||
self.config.retry_max_wait,
|
||||
)
|
||||
logger.warning(
|
||||
f"Request failed (attempt {attempt + 1}), retrying in {wait_time}s: {e}"
|
||||
)
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(
|
||||
f"Request failed after {self.config.max_retries + 1} attempts: {e}"
|
||||
)
|
||||
raise
|
||||
# This line should never be reached but satisfies type checker
|
||||
raise RuntimeError(
|
||||
"Unexpected error: retry loop exited without return or raise"
|
||||
)
|
||||
|
||||
def get_neighborhood_deals(
|
||||
self,
|
||||
polygon_id: str,
|
||||
limit: int = 10,
|
||||
start_date: Optional[str] = None,
|
||||
end_date: Optional[str] = None,
|
||||
deal_type: int = 2,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Retrieve deals within the same neighborhood as the given polygon_id.
|
||||
|
||||
Args:
|
||||
polygon_id: The ID of the lot's polygon
|
||||
limit: Maximum number of deals to return (default: 10)
|
||||
start_date: Start date for search in 'YYYY-MM' format
|
||||
end_date: End date for search in 'YYYY-MM' format
|
||||
deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
|
||||
|
||||
Returns:
|
||||
List of deals in the neighborhood
|
||||
|
||||
Raises:
|
||||
requests.RequestException: If the API request fails after retries
|
||||
ValueError: If the response or input is invalid
|
||||
"""
|
||||
if not polygon_id or not isinstance(polygon_id, str):
|
||||
raise ValueError("polygon_id must be a non-empty string")
|
||||
polygon_id = polygon_id.strip()
|
||||
if not polygon_id:
|
||||
raise ValueError("polygon_id cannot be empty or whitespace only")
|
||||
|
||||
limit = self._validate_positive_int(limit, "limit", max_value=1000)
|
||||
validators.validate_deal_type(deal_type)
|
||||
|
||||
url = f"{self.base_url}/real-estate/neighborhood-deals/{polygon_id}"
|
||||
|
||||
params: Dict[str, Any] = {"limit": limit, "dealType": deal_type}
|
||||
if start_date:
|
||||
params["startDate"] = start_date
|
||||
if end_date:
|
||||
params["endDate"] = end_date
|
||||
|
||||
# Retry logic with exponential backoff
|
||||
for attempt in range(self.config.max_retries + 1):
|
||||
try:
|
||||
self._rate_limit()
|
||||
|
||||
logger.info(
|
||||
f"Getting neighborhood deals for polygon: {polygon_id}, dealType: {deal_type} (attempt {attempt + 1}/{self.config.max_retries + 1})"
|
||||
)
|
||||
timeout = (self.config.connect_timeout, self.config.read_timeout)
|
||||
response = self.session.get(url, params=params, timeout=timeout)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
# API returns {data: [...], totalCount: ..., limit: ..., offset: ...}
|
||||
if isinstance(data, dict) and "data" in data:
|
||||
if not isinstance(data["data"], list):
|
||||
raise ValueError(
|
||||
f"Expected list in 'data' field, got {type(data['data']).__name__}"
|
||||
)
|
||||
return data["data"]
|
||||
elif isinstance(data, list):
|
||||
return data
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unexpected response format: {type(data).__name__}"
|
||||
)
|
||||
|
||||
except (requests.RequestException, requests.Timeout) as e:
|
||||
if attempt < self.config.max_retries:
|
||||
wait_time = min(
|
||||
self.config.retry_min_wait * (2**attempt),
|
||||
self.config.retry_max_wait,
|
||||
)
|
||||
logger.warning(
|
||||
f"Request failed (attempt {attempt + 1}), retrying in {wait_time}s: {e}"
|
||||
)
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(
|
||||
f"Request failed after {self.config.max_retries + 1} attempts: {e}"
|
||||
)
|
||||
raise
|
||||
# This line should never be reached but satisfies type checker
|
||||
raise RuntimeError(
|
||||
"Unexpected error: retry loop exited without return or raise"
|
||||
)
|
||||
|
||||
def find_recent_deals_for_address(
|
||||
self,
|
||||
address: str,
|
||||
years_back: int = 2,
|
||||
radius: int = 30,
|
||||
max_deals: int = 100,
|
||||
deal_type: int = 2,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find all relevant real estate deals for a given address from the last few years.
|
||||
|
||||
This is the main use case function that ties everything together.
|
||||
Street deals include deals from the same building which get highest priority.
|
||||
|
||||
Args:
|
||||
address: The address to search for
|
||||
years_back: How many years back to search (default: 2)
|
||||
radius: Search radius in meters for initial coordinate search (default: 30)
|
||||
Small radius since street deals cover the entire street anyway
|
||||
max_deals: Maximum number of deals to return (default: 100)
|
||||
deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
|
||||
|
||||
Returns:
|
||||
List of deals found for the address area, with same building deals prioritized first,
|
||||
then street deals, then neighborhood deals
|
||||
|
||||
Raises:
|
||||
ValueError: If address cannot be found or processed, or input is invalid
|
||||
requests.RequestException: If API requests fail after retries
|
||||
"""
|
||||
# Validate inputs
|
||||
address = self._validate_address(address)
|
||||
years_back = self._validate_positive_int(years_back, "years_back", max_value=50)
|
||||
radius = self._validate_positive_int(radius, "radius", max_value=5000)
|
||||
max_deals = self._validate_positive_int(max_deals, "max_deals", max_value=10000)
|
||||
validators.validate_deal_type(deal_type)
|
||||
|
||||
try:
|
||||
# Step 1: Get coordinates for the address
|
||||
logger.info(
|
||||
f"Starting search for address: {address}, dealType: {deal_type}"
|
||||
)
|
||||
autocomplete_result = self.autocomplete_address(address)
|
||||
|
||||
if not autocomplete_result.get("results"):
|
||||
raise ValueError(f"No results found for address: {address}")
|
||||
|
||||
# Get the best match (first result)
|
||||
best_match = autocomplete_result["results"][0]
|
||||
if "shape" not in best_match:
|
||||
raise ValueError("No coordinates found in autocomplete result")
|
||||
|
||||
# Parse coordinates from WKT POINT string
|
||||
# Format: "POINT(longitude latitude)"
|
||||
shape_str = best_match["shape"]
|
||||
if not shape_str.startswith("POINT("):
|
||||
raise ValueError("Invalid coordinate format in autocomplete result")
|
||||
|
||||
# Extract coordinates from "POINT(x y)"
|
||||
coords_str = shape_str[6:-1] # Remove "POINT(" and ")"
|
||||
coords = coords_str.split()
|
||||
if len(coords) != 2:
|
||||
raise ValueError("Invalid coordinate format in autocomplete result")
|
||||
|
||||
point = (float(coords[0]), float(coords[1]))
|
||||
search_address_normalized = address.lower().strip()
|
||||
logger.info(f"Found coordinates: {point}")
|
||||
|
||||
# Step 2: Get deals by radius to find polygon IDs
|
||||
nearby_deals = self.get_deals_by_radius(point, radius=radius)
|
||||
|
||||
# Extract unique polygon IDs
|
||||
polygon_ids = set()
|
||||
for deal in nearby_deals:
|
||||
if "polygon_id" in deal:
|
||||
polygon_ids.add(str(deal["polygon_id"]))
|
||||
|
||||
logger.info(f"Found {len(polygon_ids)} unique polygon IDs")
|
||||
|
||||
# Step 3: Calculate date range
|
||||
end_date = datetime.now()
|
||||
start_date = end_date - timedelta(days=years_back * 365)
|
||||
start_date_str = start_date.strftime("%Y-%m")
|
||||
end_date_str = end_date.strftime("%Y-%m")
|
||||
|
||||
# Step 4: Get street and neighborhood deals for each polygon
|
||||
# Prioritize: same building (0) > street deals (1) > neighborhood deals (2)
|
||||
building_deals = []
|
||||
street_deals = []
|
||||
neighborhood_deals = []
|
||||
seen_deals = set() # For deduplication
|
||||
|
||||
for polygon_id in polygon_ids:
|
||||
try:
|
||||
# Get street deals first (higher priority)
|
||||
current_street_deals = self.get_street_deals(
|
||||
polygon_id,
|
||||
limit=max_deals // 2, # Allocate more to street deals
|
||||
start_date=start_date_str,
|
||||
end_date=end_date_str,
|
||||
deal_type=deal_type,
|
||||
)
|
||||
|
||||
# Get neighborhood deals (lower priority)
|
||||
current_neighborhood_deals = self.get_neighborhood_deals(
|
||||
polygon_id,
|
||||
limit=max_deals // 4, # Allocate less to neighborhood deals
|
||||
start_date=start_date_str,
|
||||
end_date=end_date_str,
|
||||
deal_type=deal_type,
|
||||
)
|
||||
|
||||
# Process street deals and separate building deals
|
||||
for deal in current_street_deals:
|
||||
# Create unique deal ID for deduplication
|
||||
deal_id = f"{deal.get('dealId', '')}{deal.get('dealDate', '')}"
|
||||
if deal_id not in seen_deals:
|
||||
seen_deals.add(deal_id)
|
||||
deal["source_polygon_id"] = polygon_id
|
||||
deal["deal_source"] = "street"
|
||||
|
||||
# Check if this is from the same building
|
||||
# Construct address from API fields (API doesn't have single "address" field)
|
||||
street = deal.get("streetNameHeb", "")
|
||||
house_num = str(deal.get("houseNum", ""))
|
||||
deal_address = f"{street} {house_num}".lower().strip()
|
||||
if self._is_same_building(
|
||||
search_address_normalized, deal_address
|
||||
):
|
||||
deal["deal_source"] = "same_building"
|
||||
deal["priority"] = 0 # Highest priority
|
||||
building_deals.append(deal)
|
||||
else:
|
||||
deal["priority"] = 1 # Street deals priority
|
||||
street_deals.append(deal)
|
||||
|
||||
# Add neighborhood deals with lowest priority
|
||||
for deal in current_neighborhood_deals:
|
||||
# Create unique deal ID for deduplication
|
||||
deal_id = f"{deal.get('dealId', '')}{deal.get('dealDate', '')}"
|
||||
if deal_id not in seen_deals:
|
||||
seen_deals.add(deal_id)
|
||||
deal["source_polygon_id"] = polygon_id
|
||||
deal["deal_source"] = "neighborhood"
|
||||
deal["priority"] = 2 # Lowest priority
|
||||
neighborhood_deals.append(deal)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error processing polygon {polygon_id}: {e}")
|
||||
continue
|
||||
|
||||
# Step 5: Combine and prioritize: building deals first, then street, then neighborhood
|
||||
all_deals = building_deals + street_deals + neighborhood_deals
|
||||
|
||||
# Use stable sort: first by date (newest first), then by priority
|
||||
# Since Python's sort is stable, the second sort maintains date order within each priority
|
||||
all_deals.sort(
|
||||
key=lambda x: x.get("dealDate", "1900-01-01"), reverse=True
|
||||
) # Newest first
|
||||
all_deals.sort(
|
||||
key=lambda x: x.get("priority", 3)
|
||||
) # Priority first (0=building, 1=street, 2=neighborhood)
|
||||
|
||||
# Limit to max_deals
|
||||
if len(all_deals) > max_deals:
|
||||
all_deals = all_deals[:max_deals]
|
||||
|
||||
# Add price per square meter calculation and deal type info
|
||||
for deal in all_deals:
|
||||
price = deal.get("dealAmount", 0)
|
||||
area = deal.get("assetArea", 0)
|
||||
if (
|
||||
isinstance(price, (int, float))
|
||||
and isinstance(area, (int, float))
|
||||
and area > 0
|
||||
):
|
||||
deal["price_per_sqm"] = round(price / area, 2)
|
||||
else:
|
||||
deal["price_per_sqm"] = None
|
||||
|
||||
# Add deal type description for clarity
|
||||
deal["deal_type"] = deal_type
|
||||
deal["deal_type_description"] = (
|
||||
"first_hand_new" if deal_type == 1 else "second_hand_used"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Found {len(all_deals)} total deals for address: {address} "
|
||||
f"(Building: {len(building_deals)}, Street: {len(street_deals)}, Neighborhood: {len(neighborhood_deals)}) "
|
||||
f"[{all_deals[0]['deal_type_description'] if all_deals else 'N/A'}]"
|
||||
)
|
||||
return all_deals
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in find_recent_deals_for_address: {e}")
|
||||
raise
|
||||
|
||||
# Filtering methods (delegate to filters module)
|
||||
def filter_deals_by_criteria(
|
||||
self,
|
||||
deals: List[Dict[str, Any]],
|
||||
property_type: Optional[str] = None,
|
||||
min_rooms: Optional[float] = None,
|
||||
max_rooms: Optional[float] = None,
|
||||
min_price: Optional[float] = None,
|
||||
max_price: Optional[float] = None,
|
||||
min_area: Optional[float] = None,
|
||||
max_area: Optional[float] = None,
|
||||
min_floor: Optional[int] = None,
|
||||
max_floor: Optional[int] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Filter deals by various criteria.
|
||||
|
||||
Delegates to filters.filter_deals_by_criteria for the actual filtering logic.
|
||||
"""
|
||||
return filters.filter_deals_by_criteria(
|
||||
deals=deals,
|
||||
property_type=property_type,
|
||||
min_rooms=min_rooms,
|
||||
max_rooms=max_rooms,
|
||||
min_price=min_price,
|
||||
max_price=max_price,
|
||||
min_area=min_area,
|
||||
max_area=max_area,
|
||||
min_floor=min_floor,
|
||||
max_floor=max_floor,
|
||||
)
|
||||
|
||||
# Statistics methods (delegate to statistics module)
|
||||
def calculate_deal_statistics(self, deals: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate statistical aggregations on deal data.
|
||||
|
||||
Delegates to statistics.calculate_deal_statistics for the actual calculations.
|
||||
"""
|
||||
return statistics.calculate_deal_statistics(deals)
|
||||
|
||||
def _calculate_std_dev(self, values: List[float]) -> float:
|
||||
"""
|
||||
Calculate standard deviation of a list of values.
|
||||
|
||||
Delegates to statistics.calculate_std_dev for the actual calculation.
|
||||
"""
|
||||
return statistics.calculate_std_dev(values)
|
||||
|
||||
# Market analysis methods (delegate to market_analysis module)
|
||||
def _parse_deal_dates(
|
||||
self, deals: List[Dict[str, Any]], time_period_months: Optional[int] = None
|
||||
):
|
||||
"""
|
||||
Parse and filter deal dates from a list of deals.
|
||||
|
||||
Delegates to market_analysis.parse_deal_dates for the actual parsing.
|
||||
"""
|
||||
return market_analysis.parse_deal_dates(deals, time_period_months)
|
||||
|
||||
def calculate_market_activity_score(
|
||||
self, deals: List[Dict[str, Any]], time_period_months: int = 12
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate market activity and liquidity metrics.
|
||||
|
||||
Delegates to market_analysis.calculate_market_activity_score for the analysis.
|
||||
"""
|
||||
return market_analysis.calculate_market_activity_score(
|
||||
deals, time_period_months
|
||||
)
|
||||
|
||||
def analyze_investment_potential(
|
||||
self, deals: List[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Analyze investment potential based on price trends and market stability.
|
||||
|
||||
Delegates to market_analysis.analyze_investment_potential for the analysis.
|
||||
"""
|
||||
return market_analysis.analyze_investment_potential(deals)
|
||||
|
||||
def get_market_liquidity(
|
||||
self, deals: List[Dict[str, Any]], time_period_months: int = 12
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get detailed market liquidity and turnover metrics.
|
||||
|
||||
Delegates to market_analysis.get_market_liquidity for the analysis.
|
||||
"""
|
||||
return market_analysis.get_market_liquidity(deals, time_period_months)
|
||||
@@ -0,0 +1,422 @@
|
||||
"""
|
||||
Market analysis functions for real estate deal data.
|
||||
|
||||
This module provides functions for analyzing market trends, activity, and investment potential.
|
||||
Focused on providing data metrics; the LLM interprets them for investment advice.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from .statistics import calculate_std_dev
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Market Activity Thresholds (deals per month)
|
||||
ACTIVITY_VERY_HIGH_THRESHOLD = 10
|
||||
ACTIVITY_HIGH_THRESHOLD = 5
|
||||
ACTIVITY_MODERATE_THRESHOLD = 3
|
||||
ACTIVITY_LOW_THRESHOLD = 1
|
||||
|
||||
# Price Volatility Thresholds (coefficient of variation %)
|
||||
VOLATILITY_VERY_VOLATILE_THRESHOLD = 50
|
||||
VOLATILITY_VOLATILE_THRESHOLD = 30
|
||||
VOLATILITY_MODERATE_THRESHOLD = 20
|
||||
VOLATILITY_STABLE_THRESHOLD = 10
|
||||
|
||||
# Liquidity Thresholds (deals per month)
|
||||
LIQUIDITY_VERY_HIGH_THRESHOLD = 8
|
||||
LIQUIDITY_HIGH_THRESHOLD = 5
|
||||
LIQUIDITY_MODERATE_THRESHOLD = 2
|
||||
LIQUIDITY_LOW_THRESHOLD = 0.5
|
||||
|
||||
|
||||
def parse_deal_dates(
|
||||
deals: List[Dict[str, Any]], time_period_months: Optional[int] = None
|
||||
) -> Tuple[List[str], Dict[str, int], Dict[str, int]]:
|
||||
"""
|
||||
Parse and filter deal dates from a list of deals.
|
||||
|
||||
This helper method centralizes the date parsing logic used across
|
||||
multiple market analysis functions. It validates dates, filters by
|
||||
time period if specified, and groups deals by month and quarter.
|
||||
|
||||
Args:
|
||||
deals: List of deal dictionaries with 'dealDate' field
|
||||
time_period_months: Optional time period to filter (from today backwards)
|
||||
|
||||
Returns:
|
||||
Tuple containing:
|
||||
- List of valid deal date strings
|
||||
- Dictionary mapping year-month to deal counts
|
||||
- Dictionary mapping year-quarter to deal counts
|
||||
|
||||
Raises:
|
||||
ValueError: If no valid deal dates are found
|
||||
"""
|
||||
# Calculate cutoff date if time period is specified
|
||||
cutoff_date = None
|
||||
if time_period_months is not None:
|
||||
cutoff_date = datetime.now() - timedelta(days=time_period_months * 30)
|
||||
cutoff_date_str = cutoff_date.strftime("%Y-%m-%d")
|
||||
|
||||
monthly_deals = defaultdict(int)
|
||||
quarterly_deals = defaultdict(int)
|
||||
deal_dates = []
|
||||
|
||||
for deal in deals:
|
||||
date_str = deal.get("dealDate", "")
|
||||
if not date_str:
|
||||
continue
|
||||
|
||||
try:
|
||||
# Filter by time period if specified
|
||||
if cutoff_date is not None and date_str < cutoff_date_str:
|
||||
continue
|
||||
|
||||
# Parse date components
|
||||
year = int(date_str[:4])
|
||||
month = int(date_str[5:7])
|
||||
quarter = (month - 1) // 3 + 1 # 1-4
|
||||
|
||||
# Track by month and quarter
|
||||
year_month = f"{year}-{month:02d}"
|
||||
year_quarter = f"{year}-Q{quarter}"
|
||||
|
||||
monthly_deals[year_month] += 1
|
||||
quarterly_deals[year_quarter] += 1
|
||||
deal_dates.append(date_str)
|
||||
except (ValueError, IndexError):
|
||||
logger.warning(f"Invalid date format: {date_str}")
|
||||
continue
|
||||
|
||||
if not deal_dates:
|
||||
raise ValueError("No valid deal dates found in deals list")
|
||||
|
||||
return deal_dates, dict(monthly_deals), dict(quarterly_deals)
|
||||
|
||||
|
||||
def calculate_market_activity_score(
|
||||
deals: List[Dict[str, Any]], time_period_months: int = 12
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate market activity and liquidity metrics.
|
||||
|
||||
This function analyzes deal frequency, velocity, and market activity levels
|
||||
to provide a comprehensive view of market liquidity.
|
||||
|
||||
Args:
|
||||
deals: List of deal dictionaries
|
||||
time_period_months: Time period to analyze in months (default: 12)
|
||||
|
||||
Returns:
|
||||
Dictionary containing:
|
||||
- total_deals: Total number of deals
|
||||
- deals_per_month: Average deals per month
|
||||
- activity_score: Market activity score (0-100)
|
||||
- trend: Activity trend ('increasing', 'stable', 'decreasing')
|
||||
- monthly_distribution: Deals per month breakdown
|
||||
- activity_level: Description ('very_high', 'high', 'moderate', 'low', 'very_low')
|
||||
|
||||
Raises:
|
||||
ValueError: If deals list is empty or invalid
|
||||
"""
|
||||
if not deals:
|
||||
raise ValueError("Cannot calculate market activity from empty deals list")
|
||||
|
||||
# Parse deal dates and group by month (with time period filtering)
|
||||
deal_dates, monthly_deals, _ = parse_deal_dates(deals, time_period_months)
|
||||
|
||||
# Calculate metrics
|
||||
total_deals = len(deal_dates)
|
||||
unique_months = len(monthly_deals)
|
||||
deals_per_month = total_deals / unique_months if unique_months > 0 else 0
|
||||
|
||||
# Calculate activity score (0-100)
|
||||
# Based on deals per month using defined thresholds
|
||||
if deals_per_month >= ACTIVITY_VERY_HIGH_THRESHOLD:
|
||||
activity_score = 100
|
||||
activity_level = "very_high"
|
||||
elif deals_per_month >= ACTIVITY_HIGH_THRESHOLD:
|
||||
activity_score = 75 + ((deals_per_month - ACTIVITY_HIGH_THRESHOLD) / ACTIVITY_HIGH_THRESHOLD) * 25
|
||||
activity_level = "high"
|
||||
elif deals_per_month >= ACTIVITY_MODERATE_THRESHOLD:
|
||||
activity_score = 50 + ((deals_per_month - ACTIVITY_MODERATE_THRESHOLD) / (ACTIVITY_HIGH_THRESHOLD - ACTIVITY_MODERATE_THRESHOLD)) * 25
|
||||
activity_level = "moderate"
|
||||
elif deals_per_month >= ACTIVITY_LOW_THRESHOLD:
|
||||
activity_score = 25 + ((deals_per_month - ACTIVITY_LOW_THRESHOLD) / (ACTIVITY_MODERATE_THRESHOLD - ACTIVITY_LOW_THRESHOLD)) * 25
|
||||
activity_level = "low"
|
||||
else:
|
||||
activity_score = deals_per_month * 25
|
||||
activity_level = "very_low"
|
||||
|
||||
# Calculate trend (compare first half vs second half)
|
||||
sorted_months = sorted(monthly_deals.keys())
|
||||
if len(sorted_months) >= 4:
|
||||
mid_point = len(sorted_months) // 2
|
||||
first_half_avg = sum(monthly_deals[m] for m in sorted_months[:mid_point]) / mid_point
|
||||
second_half_avg = sum(monthly_deals[m] for m in sorted_months[mid_point:]) / (
|
||||
len(sorted_months) - mid_point
|
||||
)
|
||||
|
||||
change_ratio = (second_half_avg - first_half_avg) / first_half_avg if first_half_avg > 0 else 0
|
||||
|
||||
if change_ratio > 0.15:
|
||||
trend = "increasing"
|
||||
elif change_ratio < -0.15:
|
||||
trend = "decreasing"
|
||||
else:
|
||||
trend = "stable"
|
||||
else:
|
||||
trend = "insufficient_data"
|
||||
|
||||
return {
|
||||
"total_deals": total_deals,
|
||||
"unique_months": unique_months,
|
||||
"deals_per_month": round(deals_per_month, 2),
|
||||
"activity_score": round(activity_score, 1),
|
||||
"activity_level": activity_level,
|
||||
"trend": trend,
|
||||
"monthly_distribution": dict(sorted(monthly_deals.items())),
|
||||
}
|
||||
|
||||
|
||||
def analyze_investment_potential(deals: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Analyze investment potential based on price trends and market stability.
|
||||
|
||||
This function calculates price appreciation rates, market volatility,
|
||||
and provides investment metrics for decision-making. The MCP provides
|
||||
data metrics; the LLM interprets them for investment advice.
|
||||
|
||||
Args:
|
||||
deals: List of deal dictionaries with price and date information
|
||||
|
||||
Returns:
|
||||
Dictionary containing:
|
||||
- price_appreciation_rate: Annual price growth rate (%)
|
||||
- price_volatility: Price volatility score (0-100, lower is more stable)
|
||||
- market_stability: Stability rating ('very_stable', 'stable', 'moderate', 'volatile', 'very_volatile')
|
||||
- price_trend: Price direction ('increasing', 'stable', 'decreasing')
|
||||
- avg_price_per_sqm: Average price per square meter
|
||||
- price_change_pct: Total price change percentage
|
||||
- investment_score: Overall investment score (0-100)
|
||||
- data_quality: Quality of data ('excellent', 'good', 'fair', 'limited')
|
||||
|
||||
Raises:
|
||||
ValueError: If deals list is empty or lacks required data
|
||||
"""
|
||||
if not deals:
|
||||
raise ValueError("Cannot analyze investment potential from empty deals list")
|
||||
|
||||
# Extract price per sqm and dates
|
||||
price_data = []
|
||||
for deal in deals:
|
||||
price_per_sqm = deal.get("price_per_sqm")
|
||||
date_str = deal.get("dealDate", "")
|
||||
|
||||
if isinstance(price_per_sqm, (int, float)) and price_per_sqm > 0 and date_str:
|
||||
try:
|
||||
# Parse date for sorting
|
||||
year = int(date_str[:4])
|
||||
month = int(date_str[5:7])
|
||||
price_data.append((year + month / 12.0, price_per_sqm))
|
||||
except (ValueError, IndexError):
|
||||
continue
|
||||
|
||||
if len(price_data) < 3:
|
||||
raise ValueError(
|
||||
"Insufficient data for investment analysis (need at least 3 valid deals with price and date)"
|
||||
)
|
||||
|
||||
# Sort by time
|
||||
price_data.sort(key=lambda x: x[0])
|
||||
times = [p[0] for p in price_data]
|
||||
prices = [p[1] for p in price_data]
|
||||
|
||||
# Calculate average price
|
||||
avg_price_per_sqm = sum(prices) / len(prices)
|
||||
|
||||
# Calculate price appreciation rate (using linear regression approximation)
|
||||
n = len(price_data)
|
||||
sum_t = sum(times)
|
||||
sum_p = sum(prices)
|
||||
sum_tp = sum(t * p for t, p in price_data)
|
||||
sum_t2 = sum(t * t for t in times)
|
||||
|
||||
# Linear regression slope
|
||||
if n * sum_t2 - sum_t * sum_t != 0:
|
||||
slope = (n * sum_tp - sum_t * sum_p) / (n * sum_t2 - sum_t * sum_t)
|
||||
# Convert to annual percentage change
|
||||
price_appreciation_rate = (slope / avg_price_per_sqm) * 100 if avg_price_per_sqm > 0 else 0
|
||||
else:
|
||||
price_appreciation_rate = 0
|
||||
|
||||
# Calculate price change from first to last deal
|
||||
if prices[0] > 0:
|
||||
price_change_pct = ((prices[-1] - prices[0]) / prices[0]) * 100
|
||||
else:
|
||||
price_change_pct = 0
|
||||
|
||||
# Determine price trend
|
||||
if price_appreciation_rate > 2:
|
||||
price_trend = "increasing"
|
||||
elif price_appreciation_rate < -2:
|
||||
price_trend = "decreasing"
|
||||
else:
|
||||
price_trend = "stable"
|
||||
|
||||
# Calculate price volatility (coefficient of variation)
|
||||
std_dev = calculate_std_dev(prices)
|
||||
if avg_price_per_sqm > 0:
|
||||
coefficient_of_variation = (std_dev / avg_price_per_sqm) * 100
|
||||
else:
|
||||
coefficient_of_variation = 0
|
||||
|
||||
# Convert CV to volatility score (0-100, lower is better)
|
||||
# Using defined volatility thresholds
|
||||
if coefficient_of_variation > VOLATILITY_VERY_VOLATILE_THRESHOLD:
|
||||
volatility_score = 100
|
||||
market_stability = "very_volatile"
|
||||
elif coefficient_of_variation > VOLATILITY_VOLATILE_THRESHOLD:
|
||||
volatility_score = 75 + ((coefficient_of_variation - VOLATILITY_VOLATILE_THRESHOLD) / (VOLATILITY_VERY_VOLATILE_THRESHOLD - VOLATILITY_VOLATILE_THRESHOLD)) * 25
|
||||
market_stability = "volatile"
|
||||
elif coefficient_of_variation > VOLATILITY_MODERATE_THRESHOLD:
|
||||
volatility_score = 50 + ((coefficient_of_variation - VOLATILITY_MODERATE_THRESHOLD) / (VOLATILITY_VOLATILE_THRESHOLD - VOLATILITY_MODERATE_THRESHOLD)) * 25
|
||||
market_stability = "moderate"
|
||||
elif coefficient_of_variation > VOLATILITY_STABLE_THRESHOLD:
|
||||
volatility_score = 25 + ((coefficient_of_variation - VOLATILITY_STABLE_THRESHOLD) / (VOLATILITY_MODERATE_THRESHOLD - VOLATILITY_STABLE_THRESHOLD)) * 25
|
||||
market_stability = "stable"
|
||||
else:
|
||||
volatility_score = (coefficient_of_variation / VOLATILITY_STABLE_THRESHOLD) * 25
|
||||
market_stability = "very_stable"
|
||||
|
||||
# Calculate investment score (0-100)
|
||||
# Positive: price appreciation, market stability (low volatility)
|
||||
# Negative: price decline, high volatility
|
||||
appreciation_component = min(max(price_appreciation_rate * 5, -25), 50) # -25 to +50
|
||||
stability_component = (100 - volatility_score) * 0.5 # 0 to 50
|
||||
|
||||
investment_score = max(0, min(100, appreciation_component + stability_component))
|
||||
|
||||
# Data quality assessment
|
||||
if n >= 20:
|
||||
data_quality = "excellent"
|
||||
elif n >= 10:
|
||||
data_quality = "good"
|
||||
elif n >= 5:
|
||||
data_quality = "fair"
|
||||
else:
|
||||
data_quality = "limited"
|
||||
|
||||
return {
|
||||
"price_appreciation_rate": round(price_appreciation_rate, 2),
|
||||
"price_volatility": round(volatility_score, 1),
|
||||
"market_stability": market_stability,
|
||||
"price_trend": price_trend,
|
||||
"avg_price_per_sqm": round(avg_price_per_sqm, 0),
|
||||
"price_change_pct": round(price_change_pct, 2),
|
||||
"investment_score": round(investment_score, 1),
|
||||
"data_quality": data_quality,
|
||||
"sample_size": n,
|
||||
}
|
||||
|
||||
|
||||
def get_market_liquidity(
|
||||
deals: List[Dict[str, Any]], time_period_months: int = 12
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get detailed market liquidity and turnover metrics.
|
||||
|
||||
This function provides granular liquidity metrics including deal velocity,
|
||||
quarterly trends, and market turnover indicators.
|
||||
|
||||
Args:
|
||||
deals: List of deal dictionaries
|
||||
time_period_months: Time period to analyze in months (default: 12)
|
||||
|
||||
Returns:
|
||||
Dictionary containing:
|
||||
- total_deals: Total number of deals in period
|
||||
- deals_per_month: Average deals per month
|
||||
- deals_per_quarter: Average deals per quarter
|
||||
- quarterly_breakdown: Deals grouped by quarter
|
||||
- velocity_score: Market velocity score (0-100)
|
||||
- liquidity_rating: Liquidity rating ('very_high', 'high', 'moderate', 'low', 'very_low')
|
||||
- trend_direction: Trend in liquidity ('improving', 'stable', 'declining')
|
||||
- most_active_period: Quarter/month with most activity
|
||||
|
||||
Raises:
|
||||
ValueError: If deals list is empty or invalid
|
||||
"""
|
||||
if not deals:
|
||||
raise ValueError("Cannot calculate market liquidity from empty deals list")
|
||||
|
||||
# Parse deal dates and group by month and quarter (with time period filtering)
|
||||
deal_dates, monthly_deals, quarterly_deals = parse_deal_dates(deals, time_period_months)
|
||||
|
||||
# Calculate metrics
|
||||
total_deals = len(deal_dates)
|
||||
unique_months = len(monthly_deals)
|
||||
unique_quarters = len(quarterly_deals)
|
||||
|
||||
deals_per_month = total_deals / unique_months if unique_months > 0 else 0
|
||||
deals_per_quarter = total_deals / unique_quarters if unique_quarters > 0 else 0
|
||||
|
||||
# Calculate velocity score (similar to activity score but focused on turnover)
|
||||
# Based on monthly deal velocity using defined thresholds
|
||||
if deals_per_month >= LIQUIDITY_VERY_HIGH_THRESHOLD:
|
||||
velocity_score = 100
|
||||
liquidity_rating = "very_high"
|
||||
elif deals_per_month >= LIQUIDITY_HIGH_THRESHOLD:
|
||||
velocity_score = 75 + ((deals_per_month - LIQUIDITY_HIGH_THRESHOLD) / (LIQUIDITY_VERY_HIGH_THRESHOLD - LIQUIDITY_HIGH_THRESHOLD)) * 25
|
||||
liquidity_rating = "high"
|
||||
elif deals_per_month >= LIQUIDITY_MODERATE_THRESHOLD:
|
||||
velocity_score = 50 + ((deals_per_month - LIQUIDITY_MODERATE_THRESHOLD) / (LIQUIDITY_HIGH_THRESHOLD - LIQUIDITY_MODERATE_THRESHOLD)) * 25
|
||||
liquidity_rating = "moderate"
|
||||
elif deals_per_month >= LIQUIDITY_LOW_THRESHOLD:
|
||||
velocity_score = 25 + ((deals_per_month - LIQUIDITY_LOW_THRESHOLD) / (LIQUIDITY_MODERATE_THRESHOLD - LIQUIDITY_LOW_THRESHOLD)) * 25
|
||||
liquidity_rating = "low"
|
||||
else:
|
||||
velocity_score = deals_per_month * 50
|
||||
liquidity_rating = "very_low"
|
||||
|
||||
# Determine trend direction (compare recent quarter to earlier quarters)
|
||||
sorted_quarters = sorted(quarterly_deals.keys())
|
||||
if len(sorted_quarters) >= 3:
|
||||
recent_quarter_avg = quarterly_deals[sorted_quarters[-1]]
|
||||
earlier_quarters_avg = sum(quarterly_deals[q] for q in sorted_quarters[:-1]) / (
|
||||
len(sorted_quarters) - 1
|
||||
)
|
||||
|
||||
if recent_quarter_avg > earlier_quarters_avg * 1.2:
|
||||
trend_direction = "improving"
|
||||
elif recent_quarter_avg < earlier_quarters_avg * 0.8:
|
||||
trend_direction = "declining"
|
||||
else:
|
||||
trend_direction = "stable"
|
||||
else:
|
||||
trend_direction = "insufficient_data"
|
||||
|
||||
# Find most active period
|
||||
if quarterly_deals:
|
||||
most_active_quarter = max(quarterly_deals.items(), key=lambda x: x[1])
|
||||
most_active_period = f"{most_active_quarter[0]} ({most_active_quarter[1]} deals)"
|
||||
else:
|
||||
most_active_period = "N/A"
|
||||
|
||||
return {
|
||||
"total_deals": total_deals,
|
||||
"unique_months": unique_months,
|
||||
"unique_quarters": unique_quarters,
|
||||
"deals_per_month": round(deals_per_month, 2),
|
||||
"deals_per_quarter": round(deals_per_quarter, 2),
|
||||
"quarterly_breakdown": dict(sorted(quarterly_deals.items())),
|
||||
"monthly_breakdown": dict(sorted(monthly_deals.items())),
|
||||
"velocity_score": round(velocity_score, 1),
|
||||
"liquidity_rating": liquidity_rating,
|
||||
"trend_direction": trend_direction,
|
||||
"most_active_period": most_active_period,
|
||||
}
|
||||
Reference in New Issue
Block a user