price-api

name	price-api
description	Fetch construction material prices from open APIs. Track price trends, regional variations, and update cost databases.
homepage	https://datadrivenconstruction.io
metadata	{"openclaw":{"emoji":"🌐","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}}

Price API for Construction Materials

Overview

Material prices fluctuate constantly. This skill fetches prices from open sources, tracks trends, and updates cost databases with current market data.

Python Implementation

import requests
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import json


class MaterialCategory():
    
    CONCRETE = 
    STEEL = 
    LUMBER = 
    COPPER = 
    ALUMINUM = 
    CEMENT = 
    AGGREGATES = 
    ASPHALT = 



 :
    
    material: 
    price: 
    unit: 
    currency: 
    source: 
    date: datetime
    region:  = 



 :
    
    material: 
    current_price: 
    week_change: 
    month_change: 
    year_change: 
    trend_direction:   


 :
    

    
    FRED_BASE = 

    
    FRED_SERIES = {
        : ,
        : ,
        : ,
        : ,
        : 
    }

     ():
        .fred_api_key = fred_api_key

     () -> [MaterialPrice]:
        

         material.lower()   .FRED_SERIES:
             []

        series_id = .FRED_SERIES[material.lower()]

         start_date  :
            start_date = (datetime.now() - timedelta(days=)).strftime()
         end_date  :
            end_date = datetime.now().strftime()

        params = {
            : series_id,
            : start_date,
            : end_date,
            : 
        }

         .fred_api_key:
            params[] = .fred_api_key

        :
            response = requests.get(.FRED_BASE, params=params)
             response.status_code != :
                 []

            data = response.json()
            observations = data.get(, [])

            prices = []
             obs  observations:
                :
                    price = (obs[])
                    prices.append(MaterialPrice(
                        material=material,
                        price=price,
                        unit=,
                        currency=,
                        source=,
                        date=datetime.strptime(obs[], ),
                        region=
                    ))
                 (ValueError, KeyError):
                    

             prices

         Exception  e:
            ()
             []

     () -> pd.DataFrame:
        
        data = [{
            : p.material,
            : p.price,
            : p.unit,
            : p.currency,
            : p.source,
            : p.date,
            : p.region
        }  p  prices]
         pd.DataFrame(data)


 :
    

    
    REGIONAL_FACTORS = {
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : 
    }

     ():
        .price_cache: [, pd.DataFrame] = {}

     () -> PriceTrend:
        

         prices.empty     prices.columns:
             

        prices = prices.sort_values()
        current = prices[].iloc[-]

        
        week_ago_idx = (prices) -   (prices) >=   
        month_ago_idx = (prices) -   (prices) >=   
        year_ago_idx = (prices) -   (prices) >=   

        week_price = prices[].iloc[week_ago_idx]
        month_price = prices[].iloc[month_ago_idx]
        year_price = prices[].iloc[year_ago_idx]

        week_change = ((current - week_price) / week_price * )  week_price  
        month_change = ((current - month_price) / month_price * )  month_price  
        year_change = ((current - year_price) / year_price * )  year_price  

        
         month_change > :
            trend = 
         month_change < -:
            trend = 
        :
            trend = 

         PriceTrend(
            material=prices[].iloc[],
            current_price=current,
            week_change=(week_change, ),
            month_change=(month_change, ),
            year_change=(year_change, ),
            trend_direction=trend
        )

     () -> :
        
        factor = .REGIONAL_FACTORS.get(region, )
         base_price * factor

     () -> pd.DataFrame:
        
        updated = cost_df.copy()

         material, price  price_updates.items():
            
            mask = updated[]..lower() == material.lower()
             mask.():
                
                old_price = updated.loc[mask, ].mean()
                factor = price / old_price  old_price >   

                
                updated.loc[mask, ] *= factor
                updated.loc[mask, date_column] = datetime.now()

         updated


 :
    

    
    REFERENCE_PRICES = {
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : ,
        : 
    }

     () -> :
        
        base_price = .REFERENCE_PRICES.get(material, )

         base_price == :
             

        
        adjusted = base_price * ( + inflation_adjustment)

        
        tracker = ConstructionPriceTracker()
         tracker.apply_regional_factor(adjusted, region)

     () -> pd.DataFrame:
        
        estimates = []
         material  materials:
            price = .estimate_price(material, region)
            estimates.append({
                : material,
                : price,
                : region,
                : ,
                : datetime.now()
            })
         pd.DataFrame(estimates)