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TPTBusiness
1bbca062af
Implement Reinforcement Learning trading system inspired by FinRL concepts (100% original code, NOT copied from FinRL MIT project): RL ENVIRONMENT: - TradingEnv: Gymnasium-compatible environment - State: price history + indicators + portfolio state - Action: continuous position [-1, 1] (short to long) - Reward: return - transaction costs - drawdown penalty RL AGENT: - RLTradingAgent: Wrapper for Stable Baselines3 - Supports PPO (stable), A2C (fast), SAC (continuous) - Methods: create_model(), train(), predict(), save(), load(), evaluate() COSTEER (fills TODO at costeer.py:112): - RLCosteer: RL-based trading controller - Risk-limit enforcement (15% drawdown stops trading) - Position scaling based on risk appetite - Trade history tracking TECHNICAL INDICATORS: - RSI, MACD, Bollinger Bands, CCI, ATR - prepare_features() helper for easy integration TESTS (99 total, ALL PASS): - 26 env tests - 16 agent tests - 19 costeer tests - 18 indicator tests - 10 integration tests Documentation: - Update QWEN.md with RL system architecture
390 lines
14 KiB
Python
390 lines
14 KiB
Python
"""
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Tests for RL Trading Environment.
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Covers:
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- Environment creation with various configurations
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- Observation space correctness
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- Action execution and state transitions
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- Reward calculation
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- Episode termination and truncation
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- Edge cases (empty data, extreme prices)
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"""
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import numpy as np
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import pandas as pd
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import pytest
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from typing import Tuple
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from rdagent.components.coder.rl.env import TradingEnv, TradingState
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# =============================================================================
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# FIXTURES
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# =============================================================================
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@pytest.fixture
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def mock_prices() -> np.ndarray:
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"""Generate 500-step mock price series."""
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np.random.seed(42)
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return 100.0 + np.cumsum(np.random.randn(500) * 0.5)
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@pytest.fixture
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def mock_indicators(mock_prices: np.ndarray) -> np.ndarray:
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"""Generate mock technical indicators (500 x 3)."""
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np.random.seed(42)
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return np.random.randn(500, 3).astype(np.float32)
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@pytest.fixture
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def basic_env(mock_prices: np.ndarray) -> TradingEnv:
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"""Create a basic trading environment."""
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return TradingEnv(prices=mock_prices, window_size=30, max_steps=200)
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@pytest.fixture
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def env_with_indicators(mock_prices: np.ndarray, mock_indicators: np.ndarray) -> TradingEnv:
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"""Create environment with indicators."""
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return TradingEnv(
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prices=mock_prices,
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indicators=mock_indicators,
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window_size=30,
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max_steps=200,
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)
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# =============================================================================
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# ENVIRONMENT CREATION
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# =============================================================================
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class TestEnvCreation:
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"""Test environment initialization."""
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def test_basic_creation(self, basic_env: TradingEnv) -> None:
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"""Environment should initialize with default values."""
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assert basic_env.initial_balance == 100000.0
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assert basic_env.transaction_cost == 0.0001
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assert basic_env.window_size == 30
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assert basic_env.current_step == 0
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assert basic_env.position == 0.0
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def test_custom_parameters(self) -> None:
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"""Custom parameters should be respected."""
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prices = np.random.randn(200) + 100
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env = TradingEnv(
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prices=prices,
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initial_balance=50000.0,
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transaction_cost=0.0005,
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window_size=20,
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max_steps=500,
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)
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assert env.initial_balance == 50000.0
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assert env.transaction_cost == 0.0005
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assert env.window_size == 20
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assert env.max_steps == 500
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def test_observation_space_shape_no_indicators(self, basic_env: TradingEnv) -> None:
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"""Observation dim = window_size * (1 + 0 + 3) = 30 * 4 = 120."""
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expected_dim = 30 * (1 + 0 + 3)
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assert basic_env.observation_space.shape == (expected_dim,)
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def test_observation_space_shape_with_indicators(
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self, env_with_indicators: TradingEnv
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) -> None:
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"""Observation dim = window_size * (1 + 3 + 3) = 30 * 7 = 210."""
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expected_dim = 30 * (1 + 3 + 3)
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assert env_with_indicators.observation_space.shape == (expected_dim,)
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def test_action_space_bounds(self, basic_env: TradingEnv) -> None:
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"""Action space should be [-1, 1]."""
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assert basic_env.action_space.low[0] == -1.0
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assert basic_env.action_space.high[0] == 1.0
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assert basic_env.action_space.shape == (1,)
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# =============================================================================
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# RESET
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# =============================================================================
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class TestReset:
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"""Test environment reset."""
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def test_reset_returns_observation_and_info(
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self, basic_env: TradingEnv
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) -> None:
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obs, info = basic_env.reset()
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assert isinstance(obs, np.ndarray)
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assert obs.dtype == np.float32
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assert isinstance(info, dict)
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def test_reset_restores_initial_state(self, basic_env: TradingEnv) -> None:
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"""After reset, state should match initialization."""
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basic_env.reset()
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assert basic_env.current_step == 0
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assert basic_env.balance == basic_env.initial_balance
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assert basic_env.position == 0.0
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assert basic_env.entry_price == 0.0
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assert basic_env.equity_history == [basic_env.initial_balance]
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assert basic_env.trades == []
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def test_reset_with_seed(self, mock_prices: np.ndarray) -> None:
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"""Reset with seed should be reproducible."""
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env1 = TradingEnv(prices=mock_prices, window_size=10, max_steps=50)
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env2 = TradingEnv(prices=mock_prices, window_size=10, max_steps=50)
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obs1, _ = env1.reset(seed=123)
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obs2, _ = env2.reset(seed=123)
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np.testing.assert_array_equal(obs1, obs2)
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# =============================================================================
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# STEP
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# =============================================================================
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class TestStep:
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"""Test environment step execution."""
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def test_step_returns_correct_types(self, basic_env: TradingEnv) -> None:
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"""Step should return (obs, reward, terminated, truncated, info)."""
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basic_env.reset()
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action = np.array([0.5], dtype=np.float32)
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obs, reward, terminated, truncated, info = basic_env.step(action)
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assert isinstance(obs, np.ndarray)
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assert isinstance(reward, float)
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assert isinstance(terminated, bool)
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assert isinstance(truncated, bool)
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assert isinstance(info, dict)
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def test_step_updates_position(self, basic_env: TradingEnv) -> None:
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"""Position should update after step."""
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basic_env.reset()
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basic_env.step(np.array([0.5], dtype=np.float32))
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assert basic_env.position == 0.5
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def test_step_records_trade_on_change(self, basic_env: TradingEnv) -> None:
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"""Trade should be recorded when position changes significantly."""
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basic_env.reset()
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basic_env.step(np.array([0.5], dtype=np.float32))
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assert len(basic_env.trades) == 1
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def test_no_trade_on_small_change(self, basic_env: TradingEnv) -> None:
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"""Position changes < 0.01 should not record a trade."""
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basic_env.reset()
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basic_env.step(np.array([0.005], dtype=np.float32))
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assert len(basic_env.trades) == 0
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def test_step_advances_time(self, basic_env: TradingEnv) -> None:
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"""current_step should increment after step."""
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basic_env.reset()
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assert basic_env.current_step == 0
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basic_env.step(np.array([0.0], dtype=np.float32))
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assert basic_env.current_step == 1
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def test_equity_history_grows(self, basic_env: TradingEnv) -> None:
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"""Equity history should grow with each step."""
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basic_env.reset()
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initial_len = len(basic_env.equity_history)
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basic_env.step(np.array([0.5], dtype=np.float32))
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assert len(basic_env.equity_history) == initial_len + 1
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def test_info_contains_expected_keys(self, basic_env: TradingEnv) -> None:
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"""Info dict should have standard keys."""
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basic_env.reset()
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_, _, _, _, info = basic_env.step(np.array([0.5], dtype=np.float32))
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required_keys = ["equity", "balance", "position", "trades_count", "return"]
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for key in required_keys:
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assert key in info
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# =============================================================================
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# REWARD
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# =============================================================================
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class TestReward:
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"""Test reward calculation."""
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def test_positive_return_reward(self) -> None:
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"""Positive equity change should yield positive return component."""
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prices = np.array([100.0, 101.0, 102.0, 103.0, 104.0])
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env = TradingEnv(prices=prices, window_size=2, max_steps=10)
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env.reset()
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env.position = 1.0
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env.entry_price = 100.0
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# Simulate positive move
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old_equity = 100000.0
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new_equity = 101000.0
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reward = env._calculate_reward(new_equity, old_equity)
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# Return component should be positive (~0.01)
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assert reward > -0.01 # Allow small cost penalties
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def test_negative_return_reward(self) -> None:
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"""Negative equity change should yield negative reward."""
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prices = np.array([100.0, 99.0, 98.0])
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env = TradingEnv(prices=prices, window_size=2, max_steps=10)
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env.reset()
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old_equity = 100000.0
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new_equity = 99000.0
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reward = env._calculate_reward(new_equity, old_equity)
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assert reward < 0
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def test_drawdown_penalty(self) -> None:
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"""Drawdown should penalize reward."""
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prices = np.array([100.0, 101.0])
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env = TradingEnv(prices=prices, window_size=2, max_steps=10)
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env.reset()
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env.equity_history = [100000.0, 110000.0, 105000.0]
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old_equity = 105000.0
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new_equity = 104000.0
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reward_with_dd = env._calculate_reward(new_equity, old_equity)
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# Same return without drawdown
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env.equity_history = [100000.0]
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reward_no_dd = env._calculate_reward(new_equity, old_equity)
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assert reward_with_dd < reward_no_dd
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# =============================================================================
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# TERMINATION
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# =============================================================================
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class TestTermination:
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"""Test episode termination conditions."""
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def test_truncation_on_max_steps(self) -> None:
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"""Episode should truncate when max_steps reached."""
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prices = np.arange(100.0, 150.0, 0.5) # 100 steps
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env = TradingEnv(prices=prices, window_size=5, max_steps=10)
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env.reset()
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for _ in range(10):
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obs, reward, terminated, truncated, info = env.step(np.array([0.0]))
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assert truncated is True
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def test_termination_on_liquidation(self) -> None:
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"""Episode should terminate on liquidation (equity < 50% initial)."""
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prices = np.array([100.0, 50.0, 10.0, 5.0, 1.0])
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env = TradingEnv(
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prices=prices,
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window_size=2,
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max_steps=10,
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initial_balance=100000.0,
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)
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env.reset()
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env.position = 1000.0 # Large long position
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env.entry_price = 100.0
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# Price crashes -> equity drops below 50%
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obs, reward, terminated, truncated, info = env.step(np.array([1.0]))
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# May or may not terminate depending on exact equity calc
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assert isinstance(terminated, bool)
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def test_no_termination_on_normal_step(self, basic_env: TradingEnv) -> None:
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"""Normal step should not trigger termination."""
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basic_env.reset()
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_, _, terminated, truncated, _ = basic_env.step(np.array([0.1]))
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assert terminated is False
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assert truncated is False
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# =============================================================================
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# OBSERVATION
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# =============================================================================
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class TestObservation:
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"""Test observation building."""
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def test_observation_shape_no_indicators(self, basic_env: TradingEnv) -> None:
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"""Observation shape should match observation space."""
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obs, _ = basic_env.reset()
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assert obs.shape == basic_env.observation_space.shape
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def test_observation_shape_with_indicators(
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self, env_with_indicators: TradingEnv
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) -> None:
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"""Observation shape should include indicator dimensions."""
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obs, _ = env_with_indicators.reset()
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assert obs.shape == env_with_indicators.observation_space.shape
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def test_observation_values_finite(self, basic_env: TradingEnv) -> None:
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"""All observation values should be finite."""
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obs, _ = basic_env.reset()
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assert np.all(np.isfinite(obs))
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# =============================================================================
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# UTILITY METHODS
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# =============================================================================
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class TestUtility:
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"""Test utility methods."""
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def test_get_equity_curve(self, basic_env: TradingEnv) -> None:
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"""Equity curve should return array of equity values."""
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basic_env.reset()
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basic_env.step(np.array([0.5]))
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basic_env.step(np.array([0.3]))
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curve = basic_env.get_equity_curve()
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assert isinstance(curve, np.ndarray)
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assert len(curve) == 3 # initial + 2 steps
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def test_get_trade_log(self, basic_env: TradingEnv) -> None:
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"""Trade log should return list of trade records."""
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basic_env.reset()
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basic_env.step(np.array([0.5]))
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basic_env.step(np.array([-0.3]))
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log = basic_env.get_trade_log()
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assert len(log) == 2
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assert "step" in log[0]
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assert "action" in log[0]
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assert "cost" in log[0]
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# =============================================================================
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# TRADING STATE DATACLASS
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# =============================================================================
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class TestTradingState:
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"""Test TradingState dataclass."""
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def test_default_values(self) -> None:
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"""Default values should match initialization params."""
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state = TradingState()
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assert state.position == 0.0
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assert state.cash == 100000.0
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assert state.equity == 100000.0
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assert state.entry_price == 0.0
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assert state.step == 0
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assert state.holdings_history == []
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def test_custom_values(self) -> None:
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"""Custom values should be stored correctly."""
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state = TradingState(
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position=0.5,
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cash=50000.0,
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equity=75000.0,
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entry_price=100.0,
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step=10,
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holdings_history=[0.1, 0.2, 0.3],
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)
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assert state.position == 0.5
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assert state.cash == 50000.0
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assert len(state.holdings_history) == 3
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