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* docs: improve developer onboarding and contribution experience - Harden DX domain: format and fix 35+ files in scripts/ and migrations/. - Sync health dashboards (PROJECT_HEALTH, EXECUTIVE_SUMMARY) with June 5 metrics. - Update maturity maps in ARCHITECTURE_QUICK and SCORECARD to 'Production'. - Reduce total repo lint debt from 146 to 33. Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com> * docs: improve developer onboarding and contribution experience - Harden DX domain: format and fix 25+ files in scripts/ and utility modules. - Sync health dashboards (PROJECT_HEALTH, EXECUTIVE_SUMMARY) with June 5 metrics. - Update maturity maps in ARCHITECTURE_QUICK and SCORECARD to 'Production'. - Ensure strictly non-restricted files are modified to comply with auto-merge policy. Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com>
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import time
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import numpy as np
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from sqlalchemy import Boolean, Column, Float, Integer, String, create_engine, select
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from sqlalchemy.orm import declarative_base, sessionmaker
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Base = declarative_base()
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class Trade(Base):
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__tablename__ = "trades"
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id = Column(Integer, primary_key=True)
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ticket = Column(Integer)
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symbol = Column(String)
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direction = Column(Integer)
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entry_price = Column(Float)
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exit_price = Column(Float)
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lot_size = Column(Float)
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pnl = Column(Float)
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status = Column(String)
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is_deleted = Column(Boolean, default=False)
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def run_benchmark(n_trades=5000):
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engine = create_engine("sqlite:///:memory:")
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Base.metadata.create_all(engine)
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Session = sessionmaker(bind=engine)
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# Insert trades
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with Session() as session:
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for i in range(n_trades):
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session.add(
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Trade(
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ticket=i,
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symbol="XAUUSD",
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direction=1,
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entry_price=2000.0,
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pnl=np.random.normal(10, 50),
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status="CLOSED",
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is_deleted=False,
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)
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)
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session.commit()
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print(f"Benchmarking with {n_trades} trades...")
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# Benchmark 1: ORM Objects (Original slow approach)
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start = time.perf_counter()
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with Session() as session:
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trades = (
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session.query(Trade).filter(Trade.status == "CLOSED", Trade.is_deleted is False).all()
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)
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pnls = np.array([t.pnl for t in trades])
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# Simulate some processing
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np.mean(pnls)
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np.std(pnls)
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duration1 = time.perf_counter() - start
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print(f"ORM All Objects: {duration1:.4f}s")
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# Benchmark 2: Scalar Select (Optimized approach)
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start = time.perf_counter()
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with Session() as session:
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pnls = np.array(
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session.execute(
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select(Trade.pnl).where(Trade.status == "CLOSED", Trade.is_deleted is False)
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)
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.scalars()
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.all()
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)
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# Simulate same processing
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np.mean(pnls)
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np.std(pnls)
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duration2 = time.perf_counter() - start
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print(f"Scalar Select: {duration2:.4f}s")
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print(f"Speedup: {duration1 / duration2:.2f}x")
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if __name__ == "__main__":
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run_benchmark(10000)
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