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Pricing Engine Toolkit

#ecommerce #python #analytics #business

Pricing Engine Toolkit

Dynamic pricing rules, competitor price monitoring, margin calculation, discount scheduling, and A/B price testing. Maximize revenue with data-driven pricing strategies.

Key Features

  • Dynamic Pricing Rules — Time-based, demand-based, and inventory-based price adjustments
  • Competitor Monitoring — Track competitor prices and auto-adjust within configured bounds
  • Margin Calculator — Real-time margin analysis across cost tiers and volume discounts
  • Discount Scheduler — Plan and auto-activate promotional pricing with start/end dates
  • A/B Price Testing — Split traffic between price variants and measure conversion impact
  • Price Guardrails — Floor and ceiling enforcement to protect margins

Quick Start 

# 1. Extract and configure
unzip pricing-engine-toolkit.zip
cd pricing-engine-toolkit
cp config.example.yaml config.yaml

# 2. Load product costs and current prices
python -m pricing_engine_toolkit.core --load-catalog catalog.csv

# 3. Run pricing optimization
python -m pricing_engine_toolkit.core --optimize --config config.yaml
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Architecture 

src/pricing_engine_toolkit/
├── __init__.py          # Package metadata and version
├── core.py              # Pricing engine: rules evaluation, price calculation
├── competitors.py       # Competitor price scraping and comparison
├── scheduler.py         # Discount and promotion scheduling
├── ab_testing.py        # Price variant assignment and result analysis
└── utils.py             # Margin math, rounding rules, currency formatting
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Usage Examples

Apply Dynamic Pricing Rules 

from pricing_engine_toolkit.core import PricingEngine

engine = PricingEngine(config_path="config.yaml")

# Evaluate pricing for a product
price = engine.calculate(
    sku="WIDGET-BLU-LG",
    base_price=49.99,
    cost=18.50,
    current_stock=12,
    demand_score=0.85  # 0-1 scale, higher = more demand
)

print(f"Recommended price: ${price['recommended']:.2f}")
print(f"Margin: {price['margin_pct']:.1%}")
print(f"Rule applied: {price['rule_name']}")
# Recommended price: $54.99
# Margin: 66.3%
# Rule applied: high_demand_low_stock
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Competitor Price Comparison 

from pricing_engine_toolkit.competitors import CompetitorTracker

tracker = CompetitorTracker(config_path="config.yaml")
comparison = tracker.compare(sku="WIDGET-BLU-LG")

for comp in comparison:
    print(f"{comp['competitor']}: ${comp['price']:.2f} "
          f"({'below' if comp['diff'] < 0 else 'above'} by ${abs(comp['diff']):.2f})")
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Schedule a Promotion 

from pricing_engine_toolkit.scheduler import DiscountScheduler

scheduler = DiscountScheduler(config_path="config.yaml")
scheduler.create_promotion(
    name="Spring Sale 2026",
    skus=["WIDGET-*"],              # Wildcard SKU matching
    discount_pct=20,
    start_date="2026-04-01",
    end_date="2026-04-14",
    min_price_floor=25.00           # Never go below this
)
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Margin Analysis Query 

-- Product margin analysis with pricing tier breakdown
SELECT
    p.sku,
    p.product_name,
    p.base_price,
    p.cost,
    p.base_price - p.cost                        AS gross_margin,
    (p.base_price - p.cost) / p.base_price * 100 AS margin_pct,
    pr.current_price,
    pr.rule_applied,
    CASE
        WHEN (p.base_price - p.cost) / p.base_price < 0.20 THEN 'LOW'
        WHEN (p.base_price - p.cost) / p.base_price < 0.40 THEN 'MEDIUM'
        ELSE 'HEALTHY'
    END AS margin_tier
FROM products p
JOIN pricing_rules pr ON p.sku = pr.sku
ORDER BY margin_pct ASC;
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Configuration 

pricing:
  strategy: "margin_optimized"      # margin_optimized | competitive | volume
  rounding: "charm"                 # charm (.99) | nearest (.00) | none
  currency: "USD"

rules:
  high_demand_low_stock:
    condition: "demand_score > 0.7 AND stock < 20"
    adjustment: "+10%"
    max_increase: 25.00

  clearance:
    condition: "stock > 500 AND days_in_stock > 90"
    adjustment: "-15%"
    min_price: "cost * 1.10"        # Never below 10% above cost

guardrails:
  min_margin_pct: 15                # Hard floor: 15% margin minimum
  max_price_change_pct: 25          # Max single adjustment: 25%
  price_change_cooldown_hours: 24   # Min time between price changes

competitors:
  check_interval_hours: 6
  sources_file: "competitors.yaml"
  strategy: "match_minus_5pct"      # match | match_minus_5pct | ignore

ab_testing:
  enabled: false
  default_split: [50, 50]           # Traffic split between variants
  min_sample_size: 200              # Minimum orders before declaring winner
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Best Practices

  1. Set margin guardrails first — Prevent rules from driving prices below profitability
  2. Start with conservative adjustments — 5-10% max change per cycle
  3. Use cooldown periods — Avoid price whiplash from rapid re-evaluations
  4. A/B test before committing — Validate that higher prices don't tank conversion
  5. Monitor competitor data quality — Stale competitor prices lead to bad decisions

Troubleshooting

Issue Cause Fix
Prices dropping too low Missing min_price guardrail Set min_margin_pct in guardrails
No rules triggering Conditions too strict Review rule conditions and thresholds
A/B test inconclusive Not enough traffic Increase min_sample_size or run longer
Competitor prices stale Scraper blocked or source changed Check competitor source configuration

This is 1 of 11 resources in the Retail Automation Pro toolkit. Get the complete [Pricing Engine Toolkit] with all files, templates, and documentation for $49.

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