main.py

import argparse
import sys

import pandas as pd

from invest import run_dca
from performance import analyse_performance
from scenarios import SCENARIOS, create_strategy_from_scenario, list_scenarios


def main():
    parser = argparse.ArgumentParser(
        description="DCA Backtesting Framework - Run scenarios",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  python main.py default
  python main.py aggressive
  python main.py --list
        """,
    )
    parser.add_argument(
        "scenario_id",
        nargs="?",
        help="ID of the scenario to run (use --list to see all available scenarios)",
    )
    parser.add_argument(
        "--list", action="store_true", help="List all available scenarios and exit"
    )

    args = parser.parse_args()

    if args.list:
        list_scenarios()
        return

    if not args.scenario_id:
        parser.print_help()
        print(
            "\nError: Please provide a scenario ID or use --list to see available scenarios"
        )
        sys.exit(1)

    if args.scenario_id not in SCENARIOS:
        print(f"Error: Scenario '{args.scenario_id}' not found.")
        print("\nAvailable scenarios:")
        for scenario_id in SCENARIOS.keys():
            print(f"  - {scenario_id}")
        sys.exit(1)

    scenario = SCENARIOS[args.scenario_id]
    print(f"Running scenario: {scenario.name}")
    print()

    strategy = create_strategy_from_scenario(scenario)

    df = pd.read_csv(strategy.dataset_path)
    df = df.set_index("time")

    lookup = df.to_dict(orient="index")

    result = run_dca(lookup, strategy)

    analyse_performance(strategy, result)


if __name__ == "__main__":
    main()


# Fractional shares -> more modern approach
# or Whole shares -> old school, depend of the constraints (if backtest for broker using whole shares)
# quant always us fractional shares