| """
|
| tests/conftest.py — Shared fixtures for pytest.
|
| """
|
| from __future__ import annotations
|
|
|
| import datetime
|
| import os
|
| import sys
|
| from pathlib import Path
|
|
|
| import numpy as np
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| import pandas as pd
|
| import pytest
|
|
|
|
|
| ROOT = Path(__file__).resolve().parent.parent
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| sys.path.insert(0, str(ROOT))
|
|
|
|
|
| os.environ.setdefault("DRY_RUN", "true")
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| os.environ.setdefault("TRADING_MODE", "paper")
|
| os.environ.setdefault("ALPACA_API_KEY", "test_key")
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| os.environ.setdefault("ALPACA_SECRET_KEY", "test_secret")
|
| os.environ.setdefault("ALPACA_BASE_URL", "https://paper-api.alpaca.markets")
|
| os.environ.setdefault("TELEGRAM_BOT_TOKEN", "000:TEST")
|
| os.environ.setdefault("TELEGRAM_CHAT_ID", "0")
|
| os.environ.setdefault("NEWS_API_KEY", "testkey")
|
| os.environ.setdefault("UNIVERSE", "AAPL,MSFT,NVDA")
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| os.environ.setdefault("ENV_SCHEMA_VERSION", "2")
|
| os.environ.setdefault("GPU_AVAILABLE", "false")
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| os.environ.setdefault("ALLOW_SHORT", "true")
|
| os.environ.setdefault("ACCOUNT_BALANCE_ABOVE_25K", "true")
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| os.environ.setdefault("ALPACA_ACCOUNT_TYPE", "margin")
|
| os.environ.setdefault("ALLOW_OVERNIGHT_POSITIONS", "false")
|
| os.environ.setdefault("DB_BACKEND", "sqlite")
|
|
|
|
|
| @pytest.fixture
|
| def sample_5m_bars():
|
| """Generate 200 rows of realistic 5-minute bar data."""
|
| np.random.seed(42)
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| n = 200
|
| base = 150.0
|
| timestamps = pd.date_range(
|
| start="2024-06-01 09:30",
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| periods=n,
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| freq="5min",
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| tz="US/Eastern",
|
| )
|
| close = base + np.cumsum(np.random.randn(n) * 0.3)
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| high = close + np.abs(np.random.randn(n) * 0.2)
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| low = close - np.abs(np.random.randn(n) * 0.2)
|
| open_ = close + np.random.randn(n) * 0.1
|
| volume = np.random.randint(10000, 100000, n).astype(float)
|
| vwap = (high + low + close) / 3.0
|
| trade_count = np.random.randint(50, 500, n).astype(float)
|
|
|
| df = pd.DataFrame({
|
| "timestamp": timestamps,
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| "open": open_,
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| "high": high,
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| "low": low,
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| "close": close,
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| "volume": volume,
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| "vwap": vwap,
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| "trade_count": trade_count,
|
| })
|
| return df
|
|
|
|
|
| @pytest.fixture
|
| def sample_1h_bars():
|
| """Generate 100 rows of hourly bar data."""
|
| np.random.seed(99)
|
| n = 100
|
| base = 150.0
|
| timestamps = pd.date_range(
|
| start="2024-05-01 09:30",
|
| periods=n,
|
| freq="1h",
|
| tz="US/Eastern",
|
| )
|
| close = base + np.cumsum(np.random.randn(n) * 0.5)
|
| high = close + np.abs(np.random.randn(n) * 0.3)
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| low = close - np.abs(np.random.randn(n) * 0.3)
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| open_ = close + np.random.randn(n) * 0.15
|
| volume = np.random.randint(50000, 500000, n).astype(float)
|
| vwap = (high + low + close) / 3.0
|
| trade_count = np.random.randint(200, 2000, n).astype(float)
|
|
|
| df = pd.DataFrame({
|
| "timestamp": timestamps,
|
| "open": open_,
|
| "high": high,
|
| "low": low,
|
| "close": close,
|
| "volume": volume,
|
| "vwap": vwap,
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| "trade_count": trade_count,
|
| })
|
| return df
|
|
|
|
|
| @pytest.fixture
|
| def sample_1d_bars():
|
| """Generate 252 rows of daily bar data (1 year)."""
|
| np.random.seed(7)
|
| n = 252
|
| base = 150.0
|
| timestamps = pd.bdate_range(
|
| start="2024-01-01",
|
| periods=n,
|
| )
|
| timestamps = timestamps.tz_localize("US/Eastern")
|
| close = base + np.cumsum(np.random.randn(n) * 1.5)
|
| high = close + np.abs(np.random.randn(n) * 1.0)
|
| low = close - np.abs(np.random.randn(n) * 1.0)
|
| open_ = close + np.random.randn(n) * 0.5
|
| volume = np.random.randint(1_000_000, 10_000_000, n).astype(float)
|
| vwap = (high + low + close) / 3.0
|
| trade_count = np.random.randint(1000, 10000, n).astype(float)
|
|
|
| df = pd.DataFrame({
|
| "timestamp": timestamps,
|
| "open": open_,
|
| "high": high,
|
| "low": low,
|
| "close": close,
|
| "volume": volume,
|
| "vwap": vwap,
|
| "trade_count": trade_count,
|
| })
|
| return df
|
|
|
