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TradeSense AI — Conditional Replication Unit Tests
Validates:
- Untouched 20-asset Tech/Consumer cohort definition
- Cohort evaluation pipeline execution
"""
from datetime import date, timedelta
import numpy as np
import pandas as pd
import pytest
from quant.research.conditional_replication_engine import (
ConditionalReplicationEngine,
CohortReplicationReport,
UNTOUCHED_TECH_CONSUMER_20,
)
def generate_synthetic_universe(tickers: list, n_rows: int = 250) -> dict:
np.random.seed(42)
start_date = date(2020, 1, 1)
dates = [start_date + timedelta(days=i) for i in range(n_rows)]
universe = {}
for idx, tk in enumerate(tickers):
drift = 0.0004 + (idx * 0.0001)
vol = 0.012 + (idx * 0.002)
rets = np.random.normal(drift, vol, n_rows)
close = 100.0 * np.cumprod(1.0 + rets)
high = close * 1.01
low = close * 0.99
open_p = (high + low) / 2.0
vol_shares = np.random.randint(1000000, 5000000, n_rows)
universe[tk] = pd.DataFrame({
"session": dates,
"Open": open_p,
"High": high,
"Low": low,
"Close": close,
"Volume": vol_shares,
})
return universe
class TestConditionalReplication:
def test_cohort_definition_structure(self):
tks = ConditionalReplicationEngine.get_cohort_tickers()
sec_map = ConditionalReplicationEngine.get_cohort_sector_map()
assert len(tks) == 20
assert len(sec_map) == 20
assert "QCOM" in tks
assert "BKNG" in tks
def test_evaluate_untouched_cohort_execution(self):
tks = ["QCOM", "TXN", "AVGO", "BKNG", "ABNB", "ORLY", "SPY"]
universe = generate_synthetic_universe(tks, n_rows=300)
rep: CohortReplicationReport = ConditionalReplicationEngine.evaluate_untouched_cohort(
cohort_dfs={tk: universe[tk] for tk in tks if tk != "SPY"},
spy_df=universe["SPY"],
horizon_sessions=5,
)
assert rep.n_cohort_assets == 6
assert rep.orthogonalization_audit.n_sessions > 0
assert rep.purged_walk_forward.total_folds > 0
assert isinstance(rep.is_graduation_to_robust_conditional_alpha_approved, bool)
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