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"""Tests for Combined strategy."""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from decimal import Decimal
from datetime import datetime, timezone
import pytest
from shared.models import Candle, Signal, OrderSide
from strategies.combined_strategy import CombinedStrategy
from strategies.base import BaseStrategy
class AlwaysBuyStrategy(BaseStrategy):
name = "always_buy"
@property
def warmup_period(self) -> int:
return 0
def configure(self, params: dict) -> None:
pass
def on_candle(self, candle: Candle) -> Signal | None:
return Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.BUY,
price=candle.close,
quantity=Decimal("0.01"),
reason="always buy",
)
class AlwaysSellStrategy(BaseStrategy):
name = "always_sell"
@property
def warmup_period(self) -> int:
return 0
def configure(self, params: dict) -> None:
pass
def on_candle(self, candle: Candle) -> Signal | None:
return Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=Decimal("0.01"),
reason="always sell",
)
class NeutralStrategy(BaseStrategy):
name = "neutral"
@property
def warmup_period(self) -> int:
return 0
def configure(self, params: dict) -> None:
pass
def on_candle(self, candle: Candle) -> Signal | None:
return None
def _candle(price=100.0):
return Candle(
symbol="BTCUSDT",
timeframe="1m",
open_time=datetime(2025, 1, 1, tzinfo=timezone.utc),
open=Decimal(str(price)),
high=Decimal(str(price + 10)),
low=Decimal(str(price - 10)),
close=Decimal(str(price)),
volume=Decimal("10"),
)
def test_combined_no_strategies():
c = CombinedStrategy()
c.configure({"threshold": 0.5})
assert c.on_candle(_candle()) is None
def test_combined_unanimous_buy():
c = CombinedStrategy()
c.configure({"threshold": 0.5})
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
sig = c.on_candle(_candle())
assert sig is not None
assert sig.side == OrderSide.BUY
def test_combined_unanimous_sell():
c = CombinedStrategy()
c.configure({"threshold": 0.5})
c.add_strategy(AlwaysSellStrategy(), weight=1.0)
c.add_strategy(AlwaysSellStrategy(), weight=1.0)
sig = c.on_candle(_candle())
assert sig is not None
assert sig.side == OrderSide.SELL
def test_combined_conflicting_signals_cancel():
c = CombinedStrategy()
c.configure({"threshold": 0.5})
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
c.add_strategy(AlwaysSellStrategy(), weight=1.0)
sig = c.on_candle(_candle())
assert sig is None # Score = 0, below threshold
def test_combined_weighted_buy():
c = CombinedStrategy()
c.configure({"threshold": 0.3})
c.add_strategy(AlwaysBuyStrategy(), weight=3.0)
c.add_strategy(AlwaysSellStrategy(), weight=1.0)
sig = c.on_candle(_candle())
assert sig is not None
assert sig.side == OrderSide.BUY # Score = (3-1)/4 = 0.5 >= 0.3
def test_combined_neutral_doesnt_affect_score():
c = CombinedStrategy()
c.configure({"threshold": 0.5})
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
c.add_strategy(NeutralStrategy(), weight=1.0)
sig = c.on_candle(_candle())
assert sig is not None
assert sig.side == OrderSide.BUY # Score = 1/2 = 0.5 >= 0.5
def test_combined_warmup_is_max():
c = CombinedStrategy()
c.configure({})
s1 = AlwaysBuyStrategy()
s2 = NeutralStrategy()
c.add_strategy(s1)
c.add_strategy(s2)
assert c.warmup_period == 0
def test_combined_reset_resets_all():
c = CombinedStrategy()
c.configure({})
c.add_strategy(AlwaysBuyStrategy())
c.on_candle(_candle())
c.reset() # Should not crash
def test_combined_invalid_threshold():
c = CombinedStrategy()
with pytest.raises(ValueError):
c.configure({"threshold": -1})
def test_combined_invalid_weight():
c = CombinedStrategy()
c.configure({})
with pytest.raises(ValueError):
c.add_strategy(AlwaysBuyStrategy(), weight=-1.0)
def test_combined_record_result():
"""Verify trade history tracking works correctly."""
c = CombinedStrategy()
c.configure({"adaptive_weights": True, "history_window": 5})
c.record_result("test_strat", True)
c.record_result("test_strat", False)
c.record_result("test_strat", True)
assert len(c._trade_history["test_strat"]) == 3
assert c._trade_history["test_strat"] == [True, False, True]
# Fill beyond window size to test trimming
for _ in range(5):
c.record_result("test_strat", False)
assert len(c._trade_history["test_strat"]) == 5 # Trimmed to history_window
def test_combined_adaptive_weight_increases_for_winners():
"""Strategy with high win rate gets higher effective weight."""
c = CombinedStrategy()
c.configure({"threshold": 0.3, "adaptive_weights": True, "history_window": 20})
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
# Record high win rate for always_buy (80% wins)
for _ in range(8):
c.record_result("always_buy", True)
for _ in range(2):
c.record_result("always_buy", False)
# Adaptive weight should be > base weight (1.0)
adaptive_w = c._get_adaptive_weight("always_buy", 1.0)
assert adaptive_w > 1.0
# 80% win rate -> scale = 0.5 + 0.8 = 1.3 -> weight = 1.3
assert abs(adaptive_w - 1.3) < 0.01
def test_combined_adaptive_weight_decreases_for_losers():
"""Strategy with low win rate gets lower effective weight."""
c = CombinedStrategy()
c.configure({"threshold": 0.3, "adaptive_weights": True, "history_window": 20})
c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
# Record low win rate for always_buy (20% wins)
for _ in range(2):
c.record_result("always_buy", True)
for _ in range(8):
c.record_result("always_buy", False)
# Adaptive weight should be < base weight (1.0)
adaptive_w = c._get_adaptive_weight("always_buy", 1.0)
assert adaptive_w < 1.0
# 20% win rate -> scale = 0.5 + 0.2 = 0.7 -> weight = 0.7
assert abs(adaptive_w - 0.7) < 0.01
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