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56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
import numpy as np
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import pandas as pd
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from procesing.pricers.base import PricingFunction
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class ElasticityBasedPricer(PricingFunction):
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"""
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Pricing based on demand elasticity estimates.
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f(Q, S) = base_price * (1 + alpha * elasticity * demand_deviation)
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"""
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def __init__(self, alpha: float = 0.1, price_floor: float = 0.0, price_ceil: float = np.inf):
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self.alpha = alpha
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self.price_floor = price_floor
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self.price_ceil = price_ceil
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self.elasticity = None
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self.base_prices = None
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self.mean_demand = None
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def fit(self, historical_data: pd.DataFrame):
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"""
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Calibrate from historical elasticity estimates.
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Expects: [productId, elasticity, base_price, mean_demand]
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"""
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if 'elasticity' not in historical_data.columns:
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raise ValueError("historical_data must contain 'elasticity' column")
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self.elasticity = historical_data['elasticity'].values
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self.base_prices = historical_data.get('base_price', np.ones(len(historical_data)) * 100).values
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self.mean_demand = historical_data.get('mean_demand', np.ones(len(historical_data)) * 10).values
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return self
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def predict(self, state_space) -> np.ndarray:
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"""
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Adjust prices based on demand deviation and elasticity.
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Higher demand -> increase price (but less for elastic goods)
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"""
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if self.elasticity is None:
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raise ValueError("Must call fit() before predict()")
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demand = np.asarray(state_space.demand)
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if len(demand) != len(self.elasticity):
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raise ValueError(f"Demand vector size {len(demand)} != elasticity size {len(self.elasticity)}")
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# compute demand deviation from mean
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demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
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# adjust price: if demand high and elastic, don't increase much
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# if demand high and inelastic, increase more
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price_multiplier = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
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prices = self.base_prices * price_multiplier
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# enforce bounds
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prices = np.clip(prices, self.price_floor, self.price_ceil)
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return prices
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