Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt

Use this file to discover all available pages before exploring further.

The Merchant Revenue Forecast simulation models revenue trajectory over time, considering business characteristics, market dynamics, seasonality, and customer behavior.

​
Overview

This simulation predicts merchant revenue metrics including:
  • Monthly Recurring Revenue (MRR)
  • Daily revenue
  • Customer count and churn rate
  • Average order value
  • Growth rates and market sentiment

​
Example

from upsonic.simulation import Simulation
from upsonic.simulation.scenarios import MerchantRevenueForecastSimulation

# Create simulation scenario
sim_object = MerchantRevenueForecastSimulation(
    merchant_name="TechCo",
    shareholders=["Alice", "Bob"],
    sector="E-commerce",
    location="San Francisco",
    current_monthly_revenue_usd=50000,
    current_customer_count=500,
    average_order_value=75.0
)

# Initialize simulation
simulation = Simulation(
    sim_object,
    model="anthropic/claude-sonnet-4-5",
    time_step="daily",
    simulation_duration=100,
    metrics_to_track=["monthly recurring revenue", "customer count"]
)

# Run simulation
result = simulation.run()

# Export summary report to JSON
result.report("summary").to_json("revenue_forecast_summary.json")

# Other available report file types (commented out):
# result.report("summary").to_csv("revenue_forecast_summary.csv")
# result.report("summary").to_html("revenue_forecast_summary.html")
# result.report("summary").to_pdf("revenue_forecast_summary.pdf")
# result.report("summary").show()

# Other available report types:
# result.report("detailed").to_json("detailed_report.json")  # Step-by-step data
# result.report("visual").to_html("charts.html")  # Interactive charts
# result.report("statistical").to_json("stats.json")  # Statistical analysis

# Save all reports at once:
# result.reports().save_all(directory="./reports", format="json")