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Implementation Plan - Options & Backtesting

Goal Description

Enhance finlearner with advanced options pricing models (Binomial, Monte Carlo) and a flexible BacktestEngine that can simulate trading strategies using both internal pre-trained models and arbitrary user-defined Python functions.

User Review Required

Note

The Agent class in agent.py will be marked as legacy/deprecated in favor of the new BacktestEngine in backtest.py, though I will keep Agent for backward compatibility or refactor it to use BacktestEngine internally if feasible.

Proposed Changes

finlearner

[MODIFY] options.py

  • Add BinomialTreePricing class for American/European options.
  • Add MonteCarloPricing class for path-dependent options (Asian) or complex payoffs.

[NEW] backtest.py

  • Create BacktestEngine class.
  • Support add_strategy(strategy_func_or_class).
  • Support run(data).
  • return BacktestResult object with metrics (Sharpe, Returns, Drawdown) and equity curve.

[MODIFY] init.py

  • Export new options classes.
  • Export BacktestEngine.

examples/examples-python

[NEW] 10_comprehensive_backtest.py

  • Demonstrate backtesting with a standard LSTM model from finlearner.
  • Demonstrate backtesting with a simple "Golden Cross" SMA python function.
  • Compare results.

Verification Plan

Automated Tests

  • Create tests/test_backtest.py to verify engine logic (entry/exit/profit calc).
  • Update tests/test_options.py to test new pricing models against known benchmarks (e.g. comparing Binomial with large N to Black-Scholes).

Manual Verification

  • Run 10_comprehensive_backtest.py and inspect console output and potential plots.