| |
| """ |
| Unit test suite for AutoReflectionEngine. |
| Verifies: |
| 1. Loading daily trade logs |
| 2. Analyzing trade attribution & mistake taxonomy |
| 3. Autonomous parameter self-tuning & RL Q-table retrain |
| 4. Markdown reflection report generation |
| """ |
|
|
| import os |
| import pytest |
| import pandas as pd |
| from backend.app.ml.auto_reflection_engine import AutoReflectionEngine |
|
|
| @pytest.fixture |
| def dummy_trade_log(): |
| return pd.DataFrame([ |
| {"pnl": 15.5, "action": "buy", "ticker": "SNDK"}, |
| {"pnl": -2.5, "action": "buy", "ticker": "SNDK"}, |
| {"pnl": -8.0, "action": "buy", "ticker": "MU"}, |
| {"pnl": 3.2, "action": "sell", "ticker": "NVDA"}, |
| {"pnl": -12.0, "action": "buy", "ticker": "TSLA"} |
| ]) |
|
|
| def test_trade_attribution(dummy_trade_log): |
| engine = AutoReflectionEngine() |
| attr = engine.analyze_trade_attribution(dummy_trade_log) |
|
|
| assert attr["total_trades"] == 5 |
| assert attr["winning_trades"] == 2 |
| assert attr["losing_trades"] == 3 |
| assert attr["win_rate_%"] == 40.0 |
| assert "taxonomy_breakdown" in attr |
| assert attr["taxonomy_breakdown"]["Optimal_Profit"] == 1 |
| assert attr["taxonomy_breakdown"]["False_Breakout_Whipsaw"] == 2 |
|
|
| def test_autonomous_param_tuning(dummy_trade_log): |
| engine = AutoReflectionEngine() |
| attr = engine.analyze_trade_attribution(dummy_trade_log) |
| tuning = engine.autonomous_param_self_tuning(attr) |
|
|
| assert "p_win_threshold" in tuning |
| assert "atr_stop_multiplier" in tuning |
| assert "rl_q_table_retrained" in tuning |
| assert tuning["rl_q_table_retrained"] is True |
|
|
| def test_full_reflection_run(): |
| engine = AutoReflectionEngine() |
| path, attr = engine.run_daily_reflection("2026-08-12") |
| assert os.path.exists(path) |
|
|
