File size: 4,198 Bytes
9cb3002 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import sys
from pathlib import Path
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from agents.trader import QuantTrader
from env.reward import normalize_reward, compute_raw_reward
OBS_SIZE = 24 # 14 market + 5 portfolio + 5 risk
def test_sl_tp_calculation_long():
"""Test SL/TP for long (buy) positions."""
trader = QuantTrader()
obs = np.zeros(OBS_SIZE)
res_data = ("bullish", 0.8, "Signal reasoning")
fa_data = (0.7, "Sentiment reasoning")
current_price = 50000.0
risk_data = (
0.5,
{"suggested_sl_ratio": 0.02, "raw_price": current_price},
"Risk reasoning"
)
direction, size, sl, tp, reasoning = trader(obs, res_data, fa_data, risk_data)
print(f"Long: Direction: {direction}, Size: {size}")
print(f"Price: {current_price}, SL: {sl}, TP: {tp}")
if direction == 1:
assert sl < current_price, "Buy SL should be below entry"
assert tp > current_price, "Buy TP should be above entry"
expected_sl = current_price * (1 - 0.02)
assert abs(sl - expected_sl) < 1e-5, f"Expected SL {expected_sl}, got {sl}"
print("Long SL/TP test passed!")
def test_sl_tp_calculation_short():
"""Test SL/TP for short positions."""
trader = QuantTrader()
obs = np.zeros(OBS_SIZE)
res_data = ("bearish", 0.9, "Strong bearish signal")
fa_data = (0.1, "Bearish sentiment")
current_price = 50000.0
risk_data = (
0.5,
{"suggested_sl_ratio": 0.02, "raw_price": current_price},
"Risk reasoning"
)
direction, size, sl, tp, reasoning = trader(obs, res_data, fa_data, risk_data)
print(f"Short: Direction: {direction}, Size: {size}")
print(f"Price: {current_price}, SL: {sl}, TP: {tp}")
if direction == 2:
assert sl > current_price, f"Short SL should be ABOVE entry, got SL={sl}, price={current_price}"
assert tp < current_price, f"Short TP should be BELOW entry, got TP={tp}, price={current_price}"
expected_sl = current_price * (1 + 0.02)
assert abs(sl - expected_sl) < 1e-5, f"Expected SL {expected_sl}, got {sl}"
print("Short SL/TP test passed!")
else:
print(f"Trader chose direction={direction} instead of short (2). Skipping SL/TP assertions.")
def test_reward_normalization():
assert normalize_reward(1.0) > 0.0, "Positive reward should be > 0"
assert normalize_reward(-1.0) < 0.0, "Negative reward should be < 0"
assert -1.0 <= normalize_reward(100.0) <= 1.0, "Capping failed"
assert -1.0 <= normalize_reward(-100.0) <= 1.0, "Capping failed"
assert abs(normalize_reward(0.0)) < 1e-10, "Zero input should give zero"
print("Reward normalization test passed!")
def test_directional_reward():
"""Test that reward correctly handles both long and short directions."""
# Buy in uptrend = positive reward
r1 = compute_raw_reward(
profit=0.001, drawdown=0.0, volatility=0.01, sharpe=0.5,
trade_count=1, direction=1, price_trend=0.01
)
# Sell/Short in downtrend = positive reward
r2 = compute_raw_reward(
profit=0.001, drawdown=0.0, volatility=0.01, sharpe=0.5,
trade_count=1, direction=2, price_trend=-0.01
)
# Wrong direction = negative directional bonus
r3 = compute_raw_reward(
profit=0.001, drawdown=0.0, volatility=0.01, sharpe=0.5,
trade_count=1, direction=1, price_trend=-0.01
)
assert r1 > r3, f"Correct long direction ({r1}) should score higher than wrong ({r3})"
assert r2 > r3, f"Correct short direction ({r2}) should score higher than wrong ({r3})"
print(f"Directional rewards: correct_long={r1:.3f}, correct_short={r2:.3f}, wrong={r3:.3f}")
print("Directional reward test passed!")
if __name__ == "__main__":
try:
test_sl_tp_calculation_long()
test_sl_tp_calculation_short()
test_reward_normalization()
test_directional_reward()
print("\nAll math verifications passed!")
except Exception as e:
print(f"\nVerification failed: {e}")
sys.exit(1)
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