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Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 ao gong
|
3374884f57 |
@@ -15,9 +15,7 @@ v1.2.0 Phase 0：5 个基础算子 + 5 个 alpha 公式（alpha001–alpha005）
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from __future__ import annotations
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from collections.abc import Callable, Mapping
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from types import MappingProxyType
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from typing import Any, cast
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from typing import Any
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import numpy as np
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import pandas as pd
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@@ -211,367 +209,6 @@ def indneutralize(series: pd.Series, groups: pd.Series) -> pd.Series:
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return series - series.groupby(groups).transform("mean")
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# ── Phase 1 operator contract ──────────────────────────
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# This is deliberately a small, stable surface for downstream research
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# orchestration. The full alpha158 formula catalogue can continue to grow,
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# while callers use one validated dispatch entry point for the first ten
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# deterministic building blocks.
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ALPHA158_PHASE1_MAX_WINDOW = 252
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ALPHA158_PHASE1_OPERATOR_SPECS: dict[str, dict[str, Any]] = {
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"rank": {
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"name": "rank",
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"formula": "rank(series)",
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"inputs": ["series"],
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"windowed": False,
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},
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"delta": {
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"name": "delta",
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"formula": "delta(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"ts_mean": {
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"name": "ts_mean",
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"formula": "ts_mean(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"ts_std": {
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"name": "ts_std",
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"formula": "ts_std(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"ts_rank": {
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"name": "ts_rank",
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"formula": "ts_rank(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"correlation": {
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"name": "correlation",
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"formula": "correlation(series, secondary, window)",
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"inputs": ["series", "secondary"],
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"windowed": True,
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},
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"ts_min": {
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"name": "ts_min",
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"formula": "ts_min(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"ts_max": {
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"name": "ts_max",
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"formula": "ts_max(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"ts_sum": {
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"name": "ts_sum",
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"formula": "ts_sum(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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"decay_linear": {
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"name": "decay_linear",
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"formula": "decay_linear(series, window)",
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"inputs": ["series"],
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"windowed": True,
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},
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}
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_PHASE1_OPERATOR_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
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"rank": rank,
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"delta": delta,
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"ts_mean": ts_mean,
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"ts_std": ts_std,
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"ts_rank": ts_rank,
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"correlation": correlation,
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"ts_min": ts_min,
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"ts_max": ts_max,
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"ts_sum": ts_sum,
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"decay_linear": decay_linear,
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}
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def list_phase1_operators() -> tuple[str, ...]:
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"""Return the deterministic Phase 1 operator names in stable order."""
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return tuple(ALPHA158_PHASE1_OPERATOR_SPECS)
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def evaluate_phase1_operator(
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name: str,
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series: pd.Series,
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secondary: pd.Series | None = None,
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*,
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window: int | None = None,
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) -> pd.Series:
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"""Evaluate one of the ten Phase 1 operators with a validated contract.
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``window`` is required for time-series operators and forbidden for the
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cross-sectional ``rank`` operator. Binary ``correlation`` also requires
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a same-index secondary series so that callers cannot silently introduce
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alignment-dependent results.
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"""
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if name not in ALPHA158_PHASE1_OPERATOR_SPECS:
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raise KeyError(f"operator {name!r} not registered")
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is_windowed = bool(ALPHA158_PHASE1_OPERATOR_SPECS[name]["windowed"])
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if is_windowed:
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if isinstance(window, bool) or not isinstance(window, int) or window <= 0:
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raise ValueError(f"window must be a positive integer for {name}")
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if window > ALPHA158_PHASE1_MAX_WINDOW:
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raise ValueError(
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f"window exceeds maximum supported value {ALPHA158_PHASE1_MAX_WINDOW} for {name}"
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)
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if not is_windowed and window is not None:
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raise ValueError(f"window is not supported for {name}")
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if name == "correlation":
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if secondary is None:
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raise ValueError("secondary is required for correlation")
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if not series.index.equals(secondary.index):
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raise ValueError("secondary index must align with series")
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return correlation(series, secondary, window) # type: ignore[arg-type]
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if secondary is not None:
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raise ValueError(f"secondary is not supported for {name}")
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operator = _PHASE1_OPERATOR_FUNCTIONS[name]
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if name == "rank":
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return operator(series)
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return operator(series, window)
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# ── Phase 2 cumulative operator contract ──────────────────────────────
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# Phase 2 is cumulative: downstream callers can upgrade to one dispatch
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# surface covering every existing alpha158 building block, while Phase 1
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# names, metadata, ordering, and evaluation remain unchanged.
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ALPHA158_PHASE2_MAX_WINDOW = ALPHA158_PHASE1_MAX_WINDOW
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ALPHA158_PHASE2_OPERATOR_SPECS: dict[str, dict[str, Any]] = {
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name: {
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**spec,
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"parameters": ["window"] if bool(spec["windowed"]) else [],
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}
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for name, spec in ALPHA158_PHASE1_OPERATOR_SPECS.items()
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}
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ALPHA158_PHASE2_OPERATOR_SPECS.update(
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{
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"ts_argmin": {
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"name": "ts_argmin",
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"formula": "ts_argmin(series, window)",
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"inputs": ["series"],
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"parameters": ["window"],
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"windowed": True,
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},
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"ts_argmax": {
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"name": "ts_argmax",
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"formula": "ts_argmax(series, window)",
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"inputs": ["series"],
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"parameters": ["window"],
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"windowed": True,
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},
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"product": {
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"name": "product",
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"formula": "product(series, window)",
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"inputs": ["series"],
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"parameters": ["window"],
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"windowed": True,
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},
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"returns": {
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"name": "returns",
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"formula": "returns(series)",
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"inputs": ["series"],
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"parameters": [],
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"windowed": False,
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},
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"scale": {
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"name": "scale",
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"formula": "scale(series)",
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"inputs": ["series"],
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"parameters": [],
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"windowed": False,
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},
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"signed_power": {
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"name": "signed_power",
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"formula": "signed_power(series, exponent)",
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"inputs": ["series"],
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"parameters": ["exponent"],
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"windowed": False,
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},
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"stddev": {
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"name": "stddev",
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"formula": "stddev(series, window)",
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"inputs": ["series"],
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"parameters": ["window"],
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"windowed": True,
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},
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"covariance": {
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"name": "covariance",
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"formula": "covariance(series, secondary, window)",
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"inputs": ["series", "secondary"],
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"parameters": ["window"],
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"windowed": True,
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},
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"log": {
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"name": "log",
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"formula": "log(series)",
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"inputs": ["series"],
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"parameters": [],
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"windowed": False,
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},
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"abs_series": {
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"name": "abs_series",
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"formula": "abs_series(series)",
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"inputs": ["series"],
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"parameters": [],
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"windowed": False,
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},
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"sign": {
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"name": "sign",
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"formula": "sign(series)",
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"inputs": ["series"],
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"parameters": [],
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"windowed": False,
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},
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"max_pair": {
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"name": "max_pair",
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"formula": "max_pair(series, secondary)",
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"inputs": ["series", "secondary"],
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"parameters": [],
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"windowed": False,
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},
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"min_pair": {
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"name": "min_pair",
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"formula": "min_pair(series, secondary)",
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"inputs": ["series", "secondary"],
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"parameters": [],
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"windowed": False,
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},
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"indneutralize": {
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"name": "indneutralize",
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"formula": "indneutralize(series, groups)",
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"inputs": ["series", "groups"],
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"parameters": [],
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"windowed": False,
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},
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}
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)
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_PHASE2_OPERATOR_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
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**_PHASE1_OPERATOR_FUNCTIONS,
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"ts_argmin": ts_argmin,
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"ts_argmax": ts_argmax,
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"product": product,
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"returns": returns,
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"scale": scale,
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"signed_power": signed_power,
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"stddev": stddev,
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"covariance": covariance,
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"log": log,
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"abs_series": abs_series,
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"sign": sign,
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"max_pair": max_pair,
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"min_pair": min_pair,
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"indneutralize": indneutralize,
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}
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_PHASE2_WINDOWED_OPERATORS = frozenset(
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name for name, spec in ALPHA158_PHASE2_OPERATOR_SPECS.items() if bool(spec["windowed"])
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)
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_PHASE2_BINARY_OPERATORS = frozenset({"correlation", "covariance", "max_pair", "min_pair"})
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def list_phase2_operators() -> tuple[str, ...]:
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"""Return all Phase 2 operator names in stable cumulative order."""
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return tuple(ALPHA158_PHASE2_OPERATOR_SPECS)
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def _validate_phase2_window(name: str, window: int | None) -> int:
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if isinstance(window, bool) or not isinstance(window, int) or window <= 0:
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raise ValueError(f"window must be a positive integer for {name}")
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if window > ALPHA158_PHASE2_MAX_WINDOW:
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raise ValueError(
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f"window exceeds maximum supported value {ALPHA158_PHASE2_MAX_WINDOW} for {name}"
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)
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return window
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def evaluate_phase2_operator(
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name: str,
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series: pd.Series,
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secondary: pd.Series | None = None,
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*,
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window: int | None = None,
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exponent: float | None = None,
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groups: pd.Series | None = None,
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) -> pd.Series:
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"""Evaluate any existing alpha158 building block through a strict contract.
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Phase 2 rejects implicit alignment, missing required arguments, unused
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arguments, unbounded windows, and non-finite exponents before dispatch.
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"""
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if name not in ALPHA158_PHASE2_OPERATOR_SPECS:
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raise KeyError(f"operator {name!r} not registered")
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if not isinstance(series, pd.Series):
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raise TypeError("series must be a pandas Series")
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validated_window: int | None = None
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if name in _PHASE2_WINDOWED_OPERATORS:
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validated_window = _validate_phase2_window(name, window)
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elif window is not None:
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raise ValueError(f"window is not supported for {name}")
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if name in _PHASE2_BINARY_OPERATORS:
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if secondary is None:
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raise ValueError(f"secondary is required for {name}")
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if not isinstance(secondary, pd.Series):
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raise TypeError("secondary must be a pandas Series")
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if not series.index.equals(secondary.index):
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raise ValueError("secondary index must align with series")
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elif secondary is not None:
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raise ValueError(f"secondary is not supported for {name}")
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validated_exponent: float | None = None
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if name == "signed_power":
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if (
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isinstance(exponent, bool)
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or not isinstance(exponent, (int, float))
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or not np.isfinite(exponent)
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):
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raise ValueError("exponent must be a finite number for signed_power")
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validated_exponent = float(exponent)
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elif exponent is not None:
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raise ValueError(f"exponent is not supported for {name}")
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if name == "indneutralize":
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if groups is None:
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raise ValueError("groups is required for indneutralize")
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if not isinstance(groups, pd.Series):
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raise TypeError("groups must be a pandas Series")
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if not series.index.equals(groups.index):
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raise ValueError("groups index must align with series")
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elif groups is not None:
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raise ValueError(f"groups is not supported for {name}")
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operator = _PHASE2_OPERATOR_FUNCTIONS[name]
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if name == "signed_power":
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return operator(series, validated_exponent)
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if name == "indneutralize":
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return operator(series, groups)
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if name in {"correlation", "covariance"}:
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return operator(series, secondary, validated_window)
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if name in {"max_pair", "min_pair"}:
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return operator(series, secondary)
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if validated_window is not None:
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return operator(series, validated_window)
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return operator(series)
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# ── 组合算子（alpha158 公式样本） ─────────────────────────
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@@ -3093,321 +2730,6 @@ def parse_alpha_formula(formula_str: str) -> dict[str, Any]:
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return parsed
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# ── Phase 3 formula contract: frozen alpha001-alpha050 surface ──────────────
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# Formula functions remain the implementation source of truth. This contract
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# freezes their callable surface separately from formula dependencies so that
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# historical compatibility-only arguments remain explicit without rewriting
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# formulas or changing direct-call APIs.
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ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION = "1.0.0"
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ALPHA158_PHASE3_FORMULA_CATALOG_SHA256 = (
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"9a3360e5ee77a85a35d3c2fdab1efaa531fa0c263a2cb2bb5b965a1fb96fe1bd"
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)
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_PHASE3_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
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"alpha_001": alpha_001,
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"alpha_002": alpha_002,
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"alpha_003": alpha_003,
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"alpha_004": alpha_004,
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"alpha_005": alpha_005,
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"alpha_006": alpha_006,
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"alpha_007": alpha_007,
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"alpha_008": alpha_008,
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"alpha_009": alpha_009,
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"alpha_010": alpha_010,
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"alpha_011": alpha_011,
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"alpha_012": alpha_012,
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"alpha_013": alpha_013,
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"alpha_014": alpha_014,
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"alpha_015": alpha_015,
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"alpha_016": alpha_016,
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"alpha_017": alpha_017,
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"alpha_018": alpha_018,
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"alpha_019": alpha_019,
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"alpha_020": alpha_020,
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"alpha_021": alpha_021,
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"alpha_022": alpha_022,
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"alpha_023": alpha_023,
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"alpha_024": alpha_024,
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"alpha_025": alpha_025,
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"alpha_026": alpha_026,
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"alpha_027": alpha_027,
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"alpha_028": alpha_028,
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"alpha_029": alpha_029,
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"alpha_030": alpha_030,
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"alpha_031": alpha_031,
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"alpha_032": alpha_032,
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"alpha_033": alpha_033,
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"alpha_034": alpha_034,
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"alpha_035": alpha_035,
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"alpha_036": alpha_036,
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"alpha_037": alpha_037,
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"alpha_038": alpha_038,
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"alpha_039": alpha_039,
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"alpha_040": alpha_040,
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"alpha_041": alpha_041,
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"alpha_042": alpha_042,
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"alpha_043": alpha_043,
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"alpha_044": alpha_044,
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"alpha_045": alpha_045,
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"alpha_046": alpha_046,
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"alpha_047": alpha_047,
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"alpha_048": alpha_048,
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"alpha_049": alpha_049,
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"alpha_050": alpha_050,
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}
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_PHASE3_FORMULA_INPUT_OVERRIDES: dict[str, list[str]] = {
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"alpha_011": ["close", "high", "low"],
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"alpha_035": ["volume"],
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"alpha_036": ["close"],
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"alpha_040": ["high", "low"],
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"alpha_042": ["close"],
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"alpha_043": ["volume"],
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}
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_PHASE3_INPUT_CATEGORIES = {
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1: "single",
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2: "pair",
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3: "triple",
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4: "quadruple",
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}
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def _phase3_call_inputs(function: Callable[..., pd.Series]) -> list[str]:
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import inspect
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parameters = list(inspect.signature(function).parameters.values())
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if any(
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parameter.kind is not inspect.Parameter.POSITIONAL_OR_KEYWORD
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or parameter.default is not inspect.Parameter.empty
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for parameter in parameters
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):
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raise RuntimeError(f"unsupported formula signature for {function.__name__}")
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return ["open" if parameter.name == "open_" else parameter.name for parameter in parameters]
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def _phase3_string_list(meta: dict[str, Any], field: str, alpha_id: str) -> list[str]:
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value = meta[field]
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if not isinstance(value, list) or not all(isinstance(item, str) for item in value):
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raise RuntimeError(f"{field} must be a list of strings for {alpha_id}")
|
||||
return list(value)
|
||||
|
||||
|
||||
def _build_phase3_formula_specs() -> dict[str, dict[str, Any]]:
|
||||
specs: dict[str, dict[str, Any]] = {}
|
||||
for alpha_id, function in _PHASE3_FORMULA_FUNCTIONS.items():
|
||||
meta = ALPHA158_REGISTRY[alpha_id]
|
||||
call_inputs = _phase3_call_inputs(function)
|
||||
formula_inputs = _PHASE3_FORMULA_INPUT_OVERRIDES.get(alpha_id, call_inputs)
|
||||
input_category = _PHASE3_INPUT_CATEGORIES.get(len(call_inputs))
|
||||
if input_category is None:
|
||||
raise RuntimeError(f"unsupported formula input count for {alpha_id}")
|
||||
specs[alpha_id] = {
|
||||
"name": alpha_id,
|
||||
"contract_version": ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION,
|
||||
"formula": meta["formula"],
|
||||
"category": meta["category"],
|
||||
"complexity": meta["complexity"],
|
||||
"parameters": _phase3_string_list(meta, "params", alpha_id),
|
||||
"description": meta["description"],
|
||||
"references": _phase3_string_list(meta, "references", alpha_id),
|
||||
"call_inputs": list(call_inputs),
|
||||
"formula_inputs": list(formula_inputs),
|
||||
"input_category": input_category,
|
||||
}
|
||||
return specs
|
||||
|
||||
|
||||
def _freeze_phase3_formula_specs(
|
||||
specs: dict[str, dict[str, Any]],
|
||||
) -> Mapping[str, Mapping[str, Any]]:
|
||||
frozen_specs: dict[str, Mapping[str, Any]] = {}
|
||||
for alpha_id, spec in specs.items():
|
||||
frozen_specs[alpha_id] = MappingProxyType(
|
||||
{field: tuple(value) if isinstance(value, list) else value for field, value in spec.items()}
|
||||
)
|
||||
return MappingProxyType(frozen_specs)
|
||||
|
||||
|
||||
ALPHA158_PHASE3_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
|
||||
_freeze_phase3_formula_specs(_build_phase3_formula_specs())
|
||||
)
|
||||
|
||||
|
||||
def list_phase3_formulas() -> tuple[str, ...]:
|
||||
"""Return the frozen alpha001-alpha050 formula IDs in stable order."""
|
||||
return tuple(ALPHA158_PHASE3_FORMULA_SPECS)
|
||||
|
||||
|
||||
def evaluate_phase3_formula(name: str, **inputs: pd.Series) -> pd.Series:
|
||||
"""Evaluate a Phase 3 formula with an exact, alignment-safe input contract."""
|
||||
if name not in ALPHA158_PHASE3_FORMULA_SPECS:
|
||||
raise KeyError(f"formula {name!r} not registered")
|
||||
|
||||
spec = ALPHA158_PHASE3_FORMULA_SPECS[name]
|
||||
required_inputs = cast(tuple[str, ...], spec["call_inputs"])
|
||||
missing_inputs = [field for field in required_inputs if field not in inputs]
|
||||
unexpected_inputs = sorted(field for field in inputs if field not in required_inputs)
|
||||
if missing_inputs or unexpected_inputs:
|
||||
details: list[str] = []
|
||||
if missing_inputs:
|
||||
details.append(f"missing inputs {missing_inputs}")
|
||||
if unexpected_inputs:
|
||||
details.append(f"unexpected inputs {unexpected_inputs}")
|
||||
raise ValueError(f"invalid inputs for {name}: {'; '.join(details)}")
|
||||
|
||||
for field in required_inputs:
|
||||
if not isinstance(inputs[field], pd.Series):
|
||||
raise TypeError(f"{field} must be a pandas Series")
|
||||
|
||||
primary_field = required_inputs[0]
|
||||
primary = inputs[primary_field]
|
||||
for field in required_inputs[1:]:
|
||||
if not primary.index.equals(inputs[field].index):
|
||||
raise ValueError(f"{field} index must align with {primary_field}")
|
||||
|
||||
function = _PHASE3_FORMULA_FUNCTIONS[name]
|
||||
return function(*(inputs[field] for field in required_inputs))
|
||||
|
||||
|
||||
# ── Phase 4 formula contract: frozen alpha051-alpha100 surface ──────────────
|
||||
|
||||
# Phase 4 extends the versioned formula contract without mutating the Phase 3
|
||||
# catalogue, digest, dispatch surface, or the existing formula functions.
|
||||
ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION = "1.0.0"
|
||||
ALPHA158_PHASE4_FORMULA_CATALOG_SHA256 = (
|
||||
"858daf5e2abab5063fc28fcf7c79936096e2c76dde582fc7bab78b3458f17054"
|
||||
)
|
||||
|
||||
_PHASE4_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
|
||||
"alpha_051": alpha_051,
|
||||
"alpha_052": alpha_052,
|
||||
"alpha_053": alpha_053,
|
||||
"alpha_054": alpha_054,
|
||||
"alpha_055": alpha_055,
|
||||
"alpha_056": alpha_056,
|
||||
"alpha_057": alpha_057,
|
||||
"alpha_058": alpha_058,
|
||||
"alpha_059": alpha_059,
|
||||
"alpha_060": alpha_060,
|
||||
"alpha_061": alpha_061,
|
||||
"alpha_062": alpha_062,
|
||||
"alpha_063": alpha_063,
|
||||
"alpha_064": alpha_064,
|
||||
"alpha_065": alpha_065,
|
||||
"alpha_066": alpha_066,
|
||||
"alpha_067": alpha_067,
|
||||
"alpha_068": alpha_068,
|
||||
"alpha_069": alpha_069,
|
||||
"alpha_070": alpha_070,
|
||||
"alpha_071": alpha_071,
|
||||
"alpha_072": alpha_072,
|
||||
"alpha_073": alpha_073,
|
||||
"alpha_074": alpha_074,
|
||||
"alpha_075": alpha_075,
|
||||
"alpha_076": alpha_076,
|
||||
"alpha_077": alpha_077,
|
||||
"alpha_078": alpha_078,
|
||||
"alpha_079": alpha_079,
|
||||
"alpha_080": alpha_080,
|
||||
"alpha_081": alpha_081,
|
||||
"alpha_082": alpha_082,
|
||||
"alpha_083": alpha_083,
|
||||
"alpha_084": alpha_084,
|
||||
"alpha_085": alpha_085,
|
||||
"alpha_086": alpha_086,
|
||||
"alpha_087": alpha_087,
|
||||
"alpha_088": alpha_088,
|
||||
"alpha_089": alpha_089,
|
||||
"alpha_090": alpha_090,
|
||||
"alpha_091": alpha_091,
|
||||
"alpha_092": alpha_092,
|
||||
"alpha_093": alpha_093,
|
||||
"alpha_094": alpha_094,
|
||||
"alpha_095": alpha_095,
|
||||
"alpha_096": alpha_096,
|
||||
"alpha_097": alpha_097,
|
||||
"alpha_098": alpha_098,
|
||||
"alpha_099": alpha_099,
|
||||
"alpha_100": alpha_100,
|
||||
}
|
||||
|
||||
_PHASE4_INPUT_CATEGORIES = {
|
||||
1: "single",
|
||||
2: "pair",
|
||||
3: "triple",
|
||||
4: "quadruple",
|
||||
5: "quintuple",
|
||||
}
|
||||
|
||||
|
||||
def _build_phase4_formula_specs() -> dict[str, dict[str, Any]]:
|
||||
specs: dict[str, dict[str, Any]] = {}
|
||||
for alpha_id, function in _PHASE4_FORMULA_FUNCTIONS.items():
|
||||
meta = ALPHA158_REGISTRY[alpha_id]
|
||||
call_inputs = _phase3_call_inputs(function)
|
||||
formula_inputs = _phase3_string_list(meta, "inputs", alpha_id)
|
||||
input_category = _PHASE4_INPUT_CATEGORIES.get(len(call_inputs))
|
||||
if input_category is None:
|
||||
raise RuntimeError(f"unsupported formula input count for {alpha_id}")
|
||||
specs[alpha_id] = {
|
||||
"name": alpha_id,
|
||||
"contract_version": ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION,
|
||||
"formula": meta["formula"],
|
||||
"category": meta["category"],
|
||||
"complexity": meta["complexity"],
|
||||
"parameters": _phase3_string_list(meta, "params", alpha_id),
|
||||
"description": meta["description"],
|
||||
"references": _phase3_string_list(meta, "references", alpha_id),
|
||||
"call_inputs": list(call_inputs),
|
||||
"formula_inputs": formula_inputs,
|
||||
"input_category": input_category,
|
||||
}
|
||||
return specs
|
||||
|
||||
|
||||
ALPHA158_PHASE4_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
|
||||
_freeze_phase3_formula_specs(_build_phase4_formula_specs())
|
||||
)
|
||||
|
||||
|
||||
def list_phase4_formulas() -> tuple[str, ...]:
|
||||
"""Return the frozen alpha051-alpha100 formula IDs in stable order."""
|
||||
return tuple(ALPHA158_PHASE4_FORMULA_SPECS)
|
||||
|
||||
|
||||
def evaluate_phase4_formula(name: str, **inputs: pd.Series) -> pd.Series:
|
||||
"""Evaluate a Phase 4 formula with an exact, alignment-safe input contract."""
|
||||
if name not in ALPHA158_PHASE4_FORMULA_SPECS:
|
||||
raise KeyError(f"formula {name!r} not registered")
|
||||
|
||||
spec = ALPHA158_PHASE4_FORMULA_SPECS[name]
|
||||
required_inputs = cast(tuple[str, ...], spec["call_inputs"])
|
||||
missing_inputs = [field for field in required_inputs if field not in inputs]
|
||||
unexpected_inputs = sorted(field for field in inputs if field not in required_inputs)
|
||||
if missing_inputs or unexpected_inputs:
|
||||
details: list[str] = []
|
||||
if missing_inputs:
|
||||
details.append(f"missing inputs {missing_inputs}")
|
||||
if unexpected_inputs:
|
||||
details.append(f"unexpected inputs {unexpected_inputs}")
|
||||
raise ValueError(f"invalid inputs for {name}: {'; '.join(details)}")
|
||||
|
||||
for field in required_inputs:
|
||||
if not isinstance(inputs[field], pd.Series):
|
||||
raise TypeError(f"{field} must be a pandas Series")
|
||||
|
||||
primary_field = required_inputs[0]
|
||||
primary = inputs[primary_field]
|
||||
for field in required_inputs[1:]:
|
||||
if not primary.index.equals(inputs[field].index):
|
||||
raise ValueError(f"{field} index must align with {primary_field}")
|
||||
|
||||
function = _PHASE4_FORMULA_FUNCTIONS[name]
|
||||
return function(*(inputs[field] for field in required_inputs))
|
||||
|
||||
|
||||
__all__ = [
|
||||
"rank",
|
||||
"delta",
|
||||
@@ -3433,24 +2755,6 @@ __all__ = [
|
||||
"max_pair",
|
||||
"min_pair",
|
||||
"indneutralize",
|
||||
"ALPHA158_PHASE1_MAX_WINDOW",
|
||||
"ALPHA158_PHASE1_OPERATOR_SPECS",
|
||||
"list_phase1_operators",
|
||||
"evaluate_phase1_operator",
|
||||
"ALPHA158_PHASE2_MAX_WINDOW",
|
||||
"ALPHA158_PHASE2_OPERATOR_SPECS",
|
||||
"list_phase2_operators",
|
||||
"evaluate_phase2_operator",
|
||||
"ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION",
|
||||
"ALPHA158_PHASE3_FORMULA_CATALOG_SHA256",
|
||||
"ALPHA158_PHASE3_FORMULA_SPECS",
|
||||
"list_phase3_formulas",
|
||||
"evaluate_phase3_formula",
|
||||
"ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION",
|
||||
"ALPHA158_PHASE4_FORMULA_CATALOG_SHA256",
|
||||
"ALPHA158_PHASE4_FORMULA_SPECS",
|
||||
"list_phase4_formulas",
|
||||
"evaluate_phase4_formula",
|
||||
"alpha_001",
|
||||
"alpha_002",
|
||||
"alpha_003",
|
||||
|
||||
@@ -6,17 +6,8 @@ import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
import quant_engine.alpha_factors as alpha_factors_module
|
||||
from quant_engine.alpha_factors import (
|
||||
ALPHA158_REGISTRY,
|
||||
ALPHA158_PHASE1_OPERATOR_SPECS,
|
||||
ALPHA158_PHASE2_OPERATOR_SPECS,
|
||||
ALPHA158_PHASE3_FORMULA_CATALOG_SHA256,
|
||||
ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION,
|
||||
ALPHA158_PHASE3_FORMULA_SPECS,
|
||||
ALPHA158_PHASE4_FORMULA_CATALOG_SHA256,
|
||||
ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION,
|
||||
ALPHA158_PHASE4_FORMULA_SPECS,
|
||||
alpha_001,
|
||||
alpha_002,
|
||||
alpha_003,
|
||||
@@ -175,14 +166,6 @@ from quant_engine.alpha_factors import (
|
||||
alpha_156,
|
||||
alpha_157,
|
||||
alpha_158,
|
||||
evaluate_phase1_operator,
|
||||
evaluate_phase2_operator,
|
||||
evaluate_phase3_formula,
|
||||
evaluate_phase4_formula,
|
||||
list_phase1_operators,
|
||||
list_phase2_operators,
|
||||
list_phase3_formulas,
|
||||
list_phase4_formulas,
|
||||
correlation,
|
||||
covariance,
|
||||
decay_linear,
|
||||
@@ -1249,543 +1232,3 @@ def test_parse_alpha_formula_round_trip_jsonb():
|
||||
serialized = json.dumps(parsed)
|
||||
assert isinstance(serialized, str)
|
||||
assert "ts_rank" in serialized
|
||||
|
||||
|
||||
# ── v1.2.0 Phase 1: deterministic operator dispatch contract ──────────────
|
||||
|
||||
|
||||
def test_phase1_operator_catalog_is_explicit_and_serializable():
|
||||
"""Phase 1 exposes a stable, JSON-friendly catalog for downstream callers."""
|
||||
import json
|
||||
|
||||
expected = {
|
||||
"rank",
|
||||
"delta",
|
||||
"ts_mean",
|
||||
"ts_std",
|
||||
"ts_rank",
|
||||
"correlation",
|
||||
"ts_min",
|
||||
"ts_max",
|
||||
"ts_sum",
|
||||
"decay_linear",
|
||||
}
|
||||
assert set(list_phase1_operators()) == expected
|
||||
assert set(ALPHA158_PHASE1_OPERATOR_SPECS) == expected
|
||||
json.dumps(ALPHA158_PHASE1_OPERATOR_SPECS)
|
||||
for name, spec in ALPHA158_PHASE1_OPERATOR_SPECS.items():
|
||||
assert spec["name"] == name
|
||||
assert isinstance(spec["inputs"], list)
|
||||
assert isinstance(spec["formula"], str)
|
||||
|
||||
|
||||
def test_phase1_unary_operators_preserve_index_and_are_deterministic():
|
||||
values = pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"])
|
||||
|
||||
first = evaluate_phase1_operator("rank", values)
|
||||
second = evaluate_phase1_operator("rank", values)
|
||||
|
||||
pd.testing.assert_series_equal(first, second)
|
||||
assert first.index.equals(values.index)
|
||||
assert first.iloc[-1] == pytest.approx(1.0)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("name", "window"),
|
||||
[
|
||||
("delta", 2),
|
||||
("ts_mean", 2),
|
||||
("ts_std", 2),
|
||||
("ts_rank", 2),
|
||||
("ts_min", 2),
|
||||
("ts_max", 2),
|
||||
("ts_sum", 2),
|
||||
("decay_linear", 2),
|
||||
],
|
||||
)
|
||||
def test_phase1_windowed_operators_require_explicit_window(name: str, window: int):
|
||||
values = pd.Series([1.0, 2.0, 3.0, 4.0])
|
||||
|
||||
result = evaluate_phase1_operator(name, values, window=window)
|
||||
|
||||
assert result.index.equals(values.index)
|
||||
with pytest.raises(ValueError, match="window"):
|
||||
evaluate_phase1_operator(name, values)
|
||||
with pytest.raises(ValueError, match="positive integer"):
|
||||
evaluate_phase1_operator(name, values, window=1.5) # type: ignore[arg-type]
|
||||
|
||||
|
||||
def test_phase1_binary_correlation_requires_aligned_secondary_input():
|
||||
values = pd.Series([1.0, 2.0, 3.0, 4.0])
|
||||
other = pd.Series([4.0, 3.0, 2.0, 1.0])
|
||||
|
||||
result = evaluate_phase1_operator("correlation", values, other, window=2)
|
||||
|
||||
assert result.iloc[-1] == pytest.approx(-1.0)
|
||||
with pytest.raises(ValueError, match="secondary"):
|
||||
evaluate_phase1_operator("correlation", values, window=2)
|
||||
|
||||
|
||||
def test_phase1_dispatch_rejects_unknown_or_unused_arguments():
|
||||
values = pd.Series([1.0, 2.0, 3.0])
|
||||
|
||||
with pytest.raises(KeyError, match="not registered"):
|
||||
evaluate_phase1_operator("unknown", values)
|
||||
with pytest.raises(ValueError, match="window"):
|
||||
evaluate_phase1_operator("rank", values, window=2)
|
||||
with pytest.raises(ValueError, match="secondary"):
|
||||
evaluate_phase1_operator("rank", values, values)
|
||||
|
||||
|
||||
def test_phase1_dispatch_rejects_window_above_supported_limit():
|
||||
values = pd.Series([1.0, 2.0, 3.0])
|
||||
|
||||
with pytest.raises(ValueError, match="maximum"):
|
||||
evaluate_phase1_operator("ts_mean", values, window=2**63)
|
||||
|
||||
|
||||
# ── v1.2.0 Phase 2: cumulative deterministic operator contract ─────────────
|
||||
|
||||
|
||||
def test_phase2_operator_catalog_is_cumulative_stable_and_serializable():
|
||||
"""Phase 2 exposes all existing building blocks without changing Phase 1."""
|
||||
import json
|
||||
|
||||
phase1 = list_phase1_operators()
|
||||
expected_phase2 = (
|
||||
*phase1,
|
||||
"ts_argmin",
|
||||
"ts_argmax",
|
||||
"product",
|
||||
"returns",
|
||||
"scale",
|
||||
"signed_power",
|
||||
"stddev",
|
||||
"covariance",
|
||||
"log",
|
||||
"abs_series",
|
||||
"sign",
|
||||
"max_pair",
|
||||
"min_pair",
|
||||
"indneutralize",
|
||||
)
|
||||
|
||||
assert list_phase2_operators() == expected_phase2
|
||||
assert tuple(ALPHA158_PHASE2_OPERATOR_SPECS) == expected_phase2
|
||||
assert tuple(ALPHA158_PHASE1_OPERATOR_SPECS) == phase1
|
||||
json.dumps(ALPHA158_PHASE2_OPERATOR_SPECS)
|
||||
for name, spec in ALPHA158_PHASE2_OPERATOR_SPECS.items():
|
||||
assert spec["name"] == name
|
||||
assert isinstance(spec["inputs"], list)
|
||||
assert isinstance(spec["parameters"], list)
|
||||
assert isinstance(spec["formula"], str)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", ["ts_argmin", "ts_argmax", "product", "stddev"])
|
||||
def test_phase2_windowed_unary_dispatch_is_deterministic(name: str):
|
||||
values = pd.Series([3.0, 1.0, 4.0, 2.0], index=["a", "b", "c", "d"])
|
||||
|
||||
first = evaluate_phase2_operator(name, values, window=3)
|
||||
second = evaluate_phase2_operator(name, values, window=3)
|
||||
|
||||
pd.testing.assert_series_equal(first, second)
|
||||
assert first.index.equals(values.index)
|
||||
with pytest.raises(ValueError, match="window"):
|
||||
evaluate_phase2_operator(name, values)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", ["returns", "scale", "log", "abs_series", "sign"])
|
||||
def test_phase2_unary_dispatch_rejects_unused_arguments(name: str):
|
||||
values = pd.Series([1.0, 2.0, 4.0], index=["a", "b", "c"])
|
||||
|
||||
result = evaluate_phase2_operator(name, values)
|
||||
|
||||
assert result.index.equals(values.index)
|
||||
with pytest.raises(ValueError, match="window"):
|
||||
evaluate_phase2_operator(name, values, window=2)
|
||||
with pytest.raises(ValueError, match="secondary"):
|
||||
evaluate_phase2_operator(name, values, secondary=values)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("name", "window"),
|
||||
[("correlation", 2), ("covariance", 2), ("max_pair", None), ("min_pair", None)],
|
||||
)
|
||||
def test_phase2_binary_dispatch_requires_aligned_secondary(name: str, window: int | None):
|
||||
values = pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"])
|
||||
secondary = pd.Series([3.0, 2.0, 1.0], index=values.index)
|
||||
|
||||
result = evaluate_phase2_operator(name, values, secondary=secondary, window=window)
|
||||
|
||||
assert result.index.equals(values.index)
|
||||
with pytest.raises(ValueError, match="secondary is required"):
|
||||
evaluate_phase2_operator(name, values, window=window)
|
||||
with pytest.raises(ValueError, match="secondary index"):
|
||||
evaluate_phase2_operator(
|
||||
name,
|
||||
values,
|
||||
secondary=secondary.rename(index={"c": "z"}),
|
||||
window=window,
|
||||
)
|
||||
|
||||
|
||||
def test_phase2_signed_power_requires_finite_numeric_exponent():
|
||||
values = pd.Series([-4.0, 0.0, 9.0])
|
||||
|
||||
result = evaluate_phase2_operator("signed_power", values, exponent=0.5)
|
||||
|
||||
pd.testing.assert_series_equal(result, pd.Series([-2.0, 0.0, 3.0]))
|
||||
for exponent in (None, True, float("inf"), float("nan"), "2"):
|
||||
with pytest.raises(ValueError, match="exponent"):
|
||||
evaluate_phase2_operator( # type: ignore[arg-type]
|
||||
"signed_power",
|
||||
values,
|
||||
exponent=exponent,
|
||||
)
|
||||
|
||||
|
||||
def test_phase2_indneutralize_requires_aligned_groups():
|
||||
values = pd.Series([1.0, 3.0, 10.0, 14.0], index=["a", "b", "c", "d"])
|
||||
groups = pd.Series(["x", "x", "y", "y"], index=values.index)
|
||||
|
||||
result = evaluate_phase2_operator("indneutralize", values, groups=groups)
|
||||
|
||||
pd.testing.assert_series_equal(result, pd.Series([-1.0, 1.0, -2.0, 2.0], index=values.index))
|
||||
with pytest.raises(ValueError, match="groups is required"):
|
||||
evaluate_phase2_operator("indneutralize", values)
|
||||
with pytest.raises(ValueError, match="groups index"):
|
||||
evaluate_phase2_operator(
|
||||
"indneutralize",
|
||||
values,
|
||||
groups=groups.rename(index={"d": "z"}),
|
||||
)
|
||||
|
||||
|
||||
def test_phase2_dispatch_validates_primary_series_and_unused_parameters():
|
||||
values = pd.Series([1.0, 2.0, 3.0])
|
||||
|
||||
with pytest.raises(TypeError, match="series must be a pandas Series"):
|
||||
evaluate_phase2_operator("rank", [1.0, 2.0, 3.0]) # type: ignore[arg-type]
|
||||
with pytest.raises(KeyError, match="not registered"):
|
||||
evaluate_phase2_operator("unknown", values)
|
||||
with pytest.raises(ValueError, match="exponent"):
|
||||
evaluate_phase2_operator("rank", values, exponent=2.0)
|
||||
with pytest.raises(ValueError, match="groups"):
|
||||
evaluate_phase2_operator("rank", values, groups=pd.Series(["x", "x", "x"]))
|
||||
with pytest.raises(ValueError, match="maximum"):
|
||||
evaluate_phase2_operator("product", values, window=253)
|
||||
|
||||
|
||||
# ── Alpha158 Phase 3: versioned alpha001-alpha050 formula contract ──────────
|
||||
|
||||
|
||||
def _phase3_market_inputs() -> dict[str, pd.Series]:
|
||||
positions = np.arange(80, dtype=float)
|
||||
index = pd.RangeIndex(len(positions), name="row")
|
||||
open_ = pd.Series(100.0 + positions * 0.2 + np.sin(positions / 4.0), index=index)
|
||||
close = pd.Series(100.5 + positions * 0.18 + np.cos(positions / 5.0), index=index)
|
||||
high = pd.Series(np.maximum(open_, close) + 1.0, index=index)
|
||||
low = pd.Series(np.minimum(open_, close) - 1.0, index=index)
|
||||
volume = pd.Series(1_000.0 + positions**1.3 + 20.0 * np.sin(positions / 3.0), index=index)
|
||||
vwap = (open_ + close + high + low) / 4.0
|
||||
return {
|
||||
"open": open_,
|
||||
"close": close,
|
||||
"high": high,
|
||||
"low": low,
|
||||
"volume": volume,
|
||||
"vwap": vwap,
|
||||
}
|
||||
|
||||
|
||||
def test_phase3_formula_catalog_is_versioned_exact_and_content_addressed():
|
||||
import hashlib
|
||||
import json
|
||||
from collections import Counter
|
||||
|
||||
expected_ids = tuple(f"alpha_{number:03d}" for number in range(1, 51))
|
||||
expected_fields = {
|
||||
"name",
|
||||
"contract_version",
|
||||
"formula",
|
||||
"category",
|
||||
"complexity",
|
||||
"parameters",
|
||||
"description",
|
||||
"references",
|
||||
"call_inputs",
|
||||
"formula_inputs",
|
||||
"input_category",
|
||||
}
|
||||
|
||||
assert ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION == "1.0.0"
|
||||
assert list_phase3_formulas() == expected_ids
|
||||
assert tuple(ALPHA158_PHASE3_FORMULA_SPECS) == expected_ids
|
||||
assert Counter(
|
||||
spec["input_category"] for spec in ALPHA158_PHASE3_FORMULA_SPECS.values()
|
||||
) == {"single": 19, "pair": 26, "triple": 4, "quadruple": 1}
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
|
||||
assert set(spec) == expected_fields
|
||||
assert spec["name"] == alpha_id
|
||||
assert spec["contract_version"] == ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION
|
||||
assert spec["formula"] == ALPHA158_REGISTRY[alpha_id]["formula"]
|
||||
|
||||
serializable_specs = {
|
||||
alpha_id: {
|
||||
field: list(value) if isinstance(value, tuple) else value
|
||||
for field, value in spec.items()
|
||||
}
|
||||
for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items()
|
||||
}
|
||||
encoded = json.dumps(
|
||||
serializable_specs,
|
||||
sort_keys=True,
|
||||
separators=(",", ":"),
|
||||
ensure_ascii=False,
|
||||
).encode()
|
||||
assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE3_FORMULA_CATALOG_SHA256
|
||||
assert ALPHA158_PHASE3_FORMULA_CATALOG_SHA256 == (
|
||||
"9a3360e5ee77a85a35d3c2fdab1efaa531fa0c263a2cb2bb5b965a1fb96fe1bd"
|
||||
)
|
||||
|
||||
|
||||
def test_phase3_formula_catalog_is_recursively_immutable():
|
||||
import operator
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(ALPHA158_PHASE3_FORMULA_SPECS, "alpha_001", {})
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(
|
||||
ALPHA158_PHASE3_FORMULA_SPECS["alpha_001"],
|
||||
"formula",
|
||||
"changed",
|
||||
)
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(
|
||||
ALPHA158_PHASE3_FORMULA_SPECS["alpha_001"]["call_inputs"],
|
||||
0,
|
||||
"volume",
|
||||
)
|
||||
|
||||
|
||||
def test_phase3_catalog_freezes_callable_signatures_without_rewriting_formulas():
|
||||
import inspect
|
||||
|
||||
legacy_formula_input_differences = {
|
||||
"alpha_011": ("close", "high", "low"),
|
||||
"alpha_035": ("volume",),
|
||||
"alpha_036": ("close",),
|
||||
"alpha_040": ("high", "low"),
|
||||
"alpha_042": ("close",),
|
||||
"alpha_043": ("volume",),
|
||||
}
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
|
||||
function = getattr(alpha_factors_module, alpha_id)
|
||||
signature_inputs = tuple(
|
||||
"open" if name == "open_" else name
|
||||
for name in inspect.signature(function).parameters
|
||||
)
|
||||
assert spec["call_inputs"] == signature_inputs
|
||||
assert spec["formula_inputs"] == legacy_formula_input_differences.get(
|
||||
alpha_id,
|
||||
signature_inputs,
|
||||
)
|
||||
|
||||
assert ALPHA158_PHASE3_FORMULA_SPECS["alpha_035"]["call_inputs"] == (
|
||||
"close",
|
||||
"volume",
|
||||
)
|
||||
assert ALPHA158_REGISTRY["alpha_035"]["inputs"] == ["volume"]
|
||||
|
||||
|
||||
def test_phase3_dispatch_matches_all_existing_alpha001_alpha050_functions():
|
||||
inputs = _phase3_market_inputs()
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
|
||||
call_inputs = spec["call_inputs"]
|
||||
function = getattr(alpha_factors_module, alpha_id)
|
||||
expected = function(*(inputs[name] for name in call_inputs))
|
||||
actual = evaluate_phase3_formula(
|
||||
alpha_id,
|
||||
**{name: inputs[name] for name in reversed(call_inputs)},
|
||||
)
|
||||
pd.testing.assert_series_equal(actual, expected)
|
||||
|
||||
|
||||
def test_phase3_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
|
||||
inputs = _phase3_market_inputs()
|
||||
|
||||
with pytest.raises(KeyError, match="not registered"):
|
||||
evaluate_phase3_formula("alpha_051", close=inputs["close"])
|
||||
with pytest.raises(ValueError, match=r"missing inputs.*volume"):
|
||||
evaluate_phase3_formula("alpha_005", close=inputs["close"])
|
||||
with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
|
||||
evaluate_phase3_formula(
|
||||
"alpha_005",
|
||||
close=inputs["close"],
|
||||
volume=inputs["volume"],
|
||||
vwap=inputs["vwap"],
|
||||
)
|
||||
with pytest.raises(TypeError, match="close must be a pandas Series"):
|
||||
evaluate_phase3_formula( # type: ignore[arg-type]
|
||||
"alpha_005",
|
||||
close=[1.0, 2.0],
|
||||
volume=inputs["volume"],
|
||||
)
|
||||
|
||||
|
||||
def test_phase3_dispatch_rejects_implicit_series_alignment():
|
||||
inputs = _phase3_market_inputs()
|
||||
misaligned_volume = inputs["volume"].rename(index={79: 80})
|
||||
|
||||
with pytest.raises(ValueError, match="volume index must align with close"):
|
||||
evaluate_phase3_formula(
|
||||
"alpha_005",
|
||||
close=inputs["close"],
|
||||
volume=misaligned_volume,
|
||||
)
|
||||
|
||||
|
||||
# ── Alpha158 Phase 4: versioned alpha051-alpha100 formula contract ──────────
|
||||
|
||||
|
||||
def test_phase4_formula_catalog_is_versioned_exact_and_content_addressed():
|
||||
import hashlib
|
||||
import json
|
||||
from collections import Counter
|
||||
|
||||
expected_ids = tuple(f"alpha_{number:03d}" for number in range(51, 101))
|
||||
expected_fields = {
|
||||
"name",
|
||||
"contract_version",
|
||||
"formula",
|
||||
"category",
|
||||
"complexity",
|
||||
"parameters",
|
||||
"description",
|
||||
"references",
|
||||
"call_inputs",
|
||||
"formula_inputs",
|
||||
"input_category",
|
||||
}
|
||||
|
||||
assert ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION == "1.0.0"
|
||||
assert list_phase4_formulas() == expected_ids
|
||||
assert tuple(ALPHA158_PHASE4_FORMULA_SPECS) == expected_ids
|
||||
assert Counter(
|
||||
spec["input_category"] for spec in ALPHA158_PHASE4_FORMULA_SPECS.values()
|
||||
) == {"single": 1, "pair": 27, "triple": 11, "quadruple": 10, "quintuple": 1}
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
|
||||
assert set(spec) == expected_fields
|
||||
assert spec["name"] == alpha_id
|
||||
assert spec["contract_version"] == ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION
|
||||
assert spec["formula"] == ALPHA158_REGISTRY[alpha_id]["formula"]
|
||||
|
||||
serializable_specs = {
|
||||
alpha_id: {
|
||||
field: list(value) if isinstance(value, tuple) else value
|
||||
for field, value in spec.items()
|
||||
}
|
||||
for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items()
|
||||
}
|
||||
encoded = json.dumps(
|
||||
serializable_specs,
|
||||
sort_keys=True,
|
||||
separators=(",", ":"),
|
||||
ensure_ascii=False,
|
||||
).encode()
|
||||
assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE4_FORMULA_CATALOG_SHA256
|
||||
assert ALPHA158_PHASE4_FORMULA_CATALOG_SHA256 == (
|
||||
"858daf5e2abab5063fc28fcf7c79936096e2c76dde582fc7bab78b3458f17054"
|
||||
)
|
||||
|
||||
|
||||
def test_phase4_formula_catalog_is_recursively_immutable():
|
||||
import operator
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(ALPHA158_PHASE4_FORMULA_SPECS, "alpha_051", {})
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(
|
||||
ALPHA158_PHASE4_FORMULA_SPECS["alpha_051"],
|
||||
"formula",
|
||||
"changed",
|
||||
)
|
||||
with pytest.raises(TypeError):
|
||||
operator.setitem(
|
||||
ALPHA158_PHASE4_FORMULA_SPECS["alpha_051"]["call_inputs"],
|
||||
0,
|
||||
"volume",
|
||||
)
|
||||
|
||||
|
||||
def test_phase4_catalog_freezes_callable_and_formula_inputs():
|
||||
import inspect
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
|
||||
function = getattr(alpha_factors_module, alpha_id)
|
||||
signature_inputs = tuple(
|
||||
"open" if name == "open_" else name
|
||||
for name in inspect.signature(function).parameters
|
||||
)
|
||||
assert spec["call_inputs"] == signature_inputs
|
||||
assert spec["formula_inputs"] == tuple(ALPHA158_REGISTRY[alpha_id]["inputs"])
|
||||
|
||||
assert ALPHA158_PHASE4_FORMULA_SPECS["alpha_055"]["call_inputs"] == (
|
||||
"open",
|
||||
"high",
|
||||
"low",
|
||||
"volume",
|
||||
"close",
|
||||
)
|
||||
|
||||
|
||||
def test_phase4_dispatch_matches_all_existing_alpha051_alpha100_functions():
|
||||
inputs = _phase3_market_inputs()
|
||||
|
||||
for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
|
||||
call_inputs = spec["call_inputs"]
|
||||
function = getattr(alpha_factors_module, alpha_id)
|
||||
expected = function(*(inputs[name] for name in call_inputs))
|
||||
actual = evaluate_phase4_formula(
|
||||
alpha_id,
|
||||
**{name: inputs[name] for name in reversed(call_inputs)},
|
||||
)
|
||||
pd.testing.assert_series_equal(actual, expected)
|
||||
|
||||
|
||||
def test_phase4_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
|
||||
inputs = _phase3_market_inputs()
|
||||
|
||||
with pytest.raises(KeyError, match="not registered"):
|
||||
evaluate_phase4_formula("alpha_050", close=inputs["close"])
|
||||
with pytest.raises(ValueError, match=r"missing inputs.*low"):
|
||||
evaluate_phase4_formula("alpha_051", high=inputs["high"])
|
||||
with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
|
||||
evaluate_phase4_formula(
|
||||
"alpha_051",
|
||||
high=inputs["high"],
|
||||
low=inputs["low"],
|
||||
vwap=inputs["vwap"],
|
||||
)
|
||||
with pytest.raises(TypeError, match="high must be a pandas Series"):
|
||||
evaluate_phase4_formula( # type: ignore[arg-type]
|
||||
"alpha_051",
|
||||
high=[1.0, 2.0],
|
||||
low=inputs["low"],
|
||||
)
|
||||
|
||||
|
||||
def test_phase4_dispatch_rejects_implicit_series_alignment():
|
||||
inputs = _phase3_market_inputs()
|
||||
misaligned_low = inputs["low"].rename(index={79: 80})
|
||||
|
||||
with pytest.raises(ValueError, match="low index must align with high"):
|
||||
evaluate_phase4_formula(
|
||||
"alpha_051",
|
||||
high=inputs["high"],
|
||||
low=misaligned_low,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user