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Add offline workflow and coverage tests

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Yuxuan Yan
2026-06-16 17:37:16 +08:00
parent 8d908477e2
commit 31baa18ce5
16 changed files with 2104 additions and 9 deletions
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tests/helpers.py
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"""Shared deterministic test data for offline workflow tests."""
from __future__ import annotations
import numpy as np
import pandas as pd
from pipeline.common.schema import (
ALPHA_COLUMNS,
COMBO_COLUMNS,
DATA_COLUMNS,
MINUTE_BAR_COLUMNS,
)
GENERATED_SYMBOLS: tuple[str, ...] = (
"sh600000",
"sz000001",
"sh600519",
"sz300750",
)
GENERATED_SYMBOL_NAMES: dict[str, str] = {
"sh600000": "PF Bank",
"sz000001": "Ping An Bank",
"sh600519": "Kweichow Moutai",
"sz300750": "CATL",
}
def generated_sessions(n_sessions: int = 12) -> pd.DatetimeIndex:
"""Return a fixed business-day calendar used by generated fixtures."""
return pd.bdate_range("2024-01-02", periods=n_sessions)
def make_generated_daily_bars(
n_sessions: int = 12,
include_missing: bool = True,
) -> pd.DataFrame:
"""Build daily bars with explicit edge cases and no randomness.
The panel covers four A-share symbols and includes a suspended row, an ST
flag, a zero-volume row, a missing symbol-date, and limit-style open/close
moves. Values are deterministic so tests can assert exact identities.
"""
dates = generated_sessions(n_sessions)
base_close = {
"sh600000": 10.00,
"sz000001": 15.00,
"sh600519": 1200.00,
"sz300750": 180.00,
}
returns = {
"sh600000": [0.000, 0.012, -0.006, 0.018, 0.100, -0.014, 0.006, 0.000, 0.008, -0.011, 0.004, 0.009],
"sz000001": [0.000, -0.008, 0.011, -0.004, 0.006, 0.000, -0.012, 0.009, 0.005, -0.007, 0.010, -0.003],
"sh600519": [0.000, 0.006, 0.004, -0.010, 0.012, -0.006, 0.005, 0.003, -0.009, 0.007, -0.004, 0.006],
"sz300750": [0.000, -0.010, 0.014, 0.006, -0.008, 0.011, -0.004, 0.009, -0.200, 0.012, -0.006, 0.008],
}
base_volume = {
"sh600000": 1_200_000.0,
"sz000001": 900_000.0,
"sh600519": 80_000.0,
"sz300750": 240_000.0,
}
rows: list[dict[str, object]] = []
for sym in GENERATED_SYMBOLS:
closes = [base_close[sym]]
pattern = returns[sym]
for step in range(1, n_sessions):
ret = pattern[step % len(pattern)]
closes.append(closes[-1] * (1.0 + ret))
closes_arr = np.asarray(closes, dtype=float)
precloses = np.concatenate([[closes_arr[0]], closes_arr[:-1]])
for i, date in enumerate(dates):
preclose = float(precloses[i])
close = float(closes_arr[i])
open_price = preclose * (1.0 + 0.25 * (close / preclose - 1.0))
high = max(open_price, close) * 1.01
low = min(open_price, close) * 0.99
volume = base_volume[sym] + 10_000.0 * i
tradestatus = 1
is_st = 0
if sym == "sh600000" and i == 4:
open_price = preclose * 1.10
close = open_price
high = open_price
low = open_price
if sym == "sh600000" and i == 7:
volume = 0.0
if sym == "sz000001" and i == 5:
open_price = preclose
close = preclose
high = preclose
low = preclose
volume = 0.0
tradestatus = 0
if sym == "sh600519" and i == 7:
is_st = 1
if sym == "sz300750" and i == 8:
open_price = preclose * 0.80
close = open_price
high = open_price
low = open_price
amount = volume * ((open_price + close) / 2.0)
vwap = amount / volume if volume > 0 else np.nan
pct_chg = (close / preclose - 1.0) * 100.0 if preclose else 0.0
rows.append({
"symbol_id": sym,
"symbol_name": GENERATED_SYMBOL_NAMES[sym],
"date": date,
"open": open_price,
"high": high,
"low": low,
"close": close,
"preclose": preclose,
"volume": volume,
"amount": amount,
"vwap": vwap,
"turn": volume / 1_000_000.0,
"pctChg": pct_chg,
"tradestatus": tradestatus,
"isST": is_st,
"peTTM": 8.0 + i,
"pbMRQ": 1.0 + 0.05 * i,
"psTTM": 2.0 + 0.03 * i,
"pcfNcfTTM": 5.0 + 0.1 * i,
})
result = pd.DataFrame(rows)
if include_missing and n_sessions > 6:
missing_mask = (
(result["symbol_id"] == "sz300750")
& (result["date"] == dates[6])
)
result = result.loc[~missing_mask].copy()
result = result[DATA_COLUMNS]
return result.sort_values(["date", "symbol_id"]).reset_index(drop=True)
def make_generated_minute_bars(
daily: pd.DataFrame | None = None,
) -> pd.DataFrame:
"""Expand generated daily bars into a tiny deterministic intraday panel."""
daily = make_generated_daily_bars() if daily is None else daily.copy()
rows: list[dict[str, object]] = []
bar_times = ("09:35:00", "10:30:00", "14:55:00")
for daily_row in daily.sort_values(["date", "symbol_id"]).itertuples(index=False):
if int(getattr(daily_row, "tradestatus", 1)) == 0:
continue
volume = float(daily_row.volume)
volume_slices = [0.25 * volume, 0.35 * volume, 0.40 * volume]
prices = np.linspace(float(daily_row.open), float(daily_row.close), len(bar_times))
for j, time_text in enumerate(bar_times):
dt = pd.Timestamp(daily_row.date) + pd.Timedelta(time_text)
open_price = prices[j - 1] if j else float(daily_row.open)
close_price = float(prices[j])
high = max(open_price, close_price) * 1.002
low = min(open_price, close_price) * 0.998
minute_volume = float(volume_slices[j])
amount = minute_volume * ((open_price + close_price) / 2.0)
rows.append({
"symbol_id": daily_row.symbol_id,
"symbol_name": daily_row.symbol_name,
"datetime": dt,
"date": pd.Timestamp(daily_row.date).normalize(),
"time": time_text,
"frequency": "5m",
"open": open_price,
"high": high,
"low": low,
"close": close_price,
"volume": minute_volume,
"amount": amount,
"vwap": amount / minute_volume if minute_volume > 0 else np.nan,
"adjustflag": "3",
})
return pd.DataFrame(rows, columns=MINUTE_BAR_COLUMNS)
def make_generated_derived_features(
daily: pd.DataFrame | None = None,
) -> pd.DataFrame:
"""Return numeric daily derived values, including NaN and infinity cells."""
daily = make_generated_daily_bars() if daily is None else daily.copy()
keys = (
daily[["symbol_id", "date"]]
.drop_duplicates()
.sort_values(["date", "symbol_id"])
.reset_index(drop=True)
)
date_rank = keys["date"].rank(method="dense").astype(float)
symbol_rank = keys["symbol_id"].map({
"sh600000": 1.0,
"sz000001": 2.0,
"sh600519": 3.0,
"sz300750": 4.0,
})
out = keys.copy()
out["toy_feature"] = symbol_rank + date_rank / 100.0
out["finite_feature"] = symbol_rank * date_rank
out["nan_feature"] = out["toy_feature"]
out["inf_feature"] = out["toy_feature"]
if len(out) >= 2:
out.loc[out.index[0], "nan_feature"] = np.nan
out.loc[out.index[1], "inf_feature"] = np.inf
return out
def make_generated_alpha_weights(
alpha_name: str = "alpha_a",
*,
scale: float = 1.0,
offset: float = 0.0,
zero_date_index: int | None = None,
n_sessions: int = 10,
) -> pd.DataFrame:
"""Create a deterministic long alpha grid with optional zero-gross date."""
dates = generated_sessions(n_sessions)
even = np.array([1.20, -0.80, 0.40, -0.80], dtype=float)
odd = np.array([-0.60, 1.10, -0.90, 0.40], dtype=float)
rows: list[dict[str, object]] = []
for i, date in enumerate(dates):
vector = even.copy() if i % 2 == 0 else odd.copy()
vector = vector + offset
vector = vector - vector.mean()
if zero_date_index is not None and i == zero_date_index:
vector = np.zeros_like(vector)
for sym, weight in zip(GENERATED_SYMBOLS, scale * vector):
rows.append({
"symbol_id": sym,
"date": date,
"alpha_name": alpha_name,
"weight": float(weight),
})
result = pd.DataFrame(rows, columns=ALPHA_COLUMNS)
return result.sort_values(["symbol_id", "date"]).reset_index(drop=True)
def make_generated_combo_weights(
combo_name: str = "combo",
*,
zero_date_index: int | None = 2,
n_sessions: int = 10,
) -> pd.DataFrame:
"""Create deterministic combo weights for portfolio construction tests."""
alpha = make_generated_alpha_weights(
"combo_source",
zero_date_index=zero_date_index,
n_sessions=n_sessions,
)
combo = alpha.rename(columns={"alpha_name": "combo_name"}).copy()
combo["combo_name"] = combo_name
return combo[COMBO_COLUMNS].sort_values(["symbol_id", "date"]).reset_index(drop=True)