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https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-17 18:18:04 +00:00
python wrapper
This commit is contained in:
Miha Kralj
@@ -0,0 +1,306 @@
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from __future__ import annotations
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import inspect
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from pathlib import Path
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from typing import Any, Callable
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import numpy as np
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import pandas as pd
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import pandas_ta as ta
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from quantalib import indicators as q
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SEED = 42
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N = 10_000
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VERIFY_COUNT = 100
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DEFAULT_TOL = 1e-6
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REPORT_PATH = Path("python/tests/reports/pandas_ta_all_exported_report.md")
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def generate_gbm(
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n: int,
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seed: int = SEED,
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start_price: float = 100.0,
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mu: float = 0.05,
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sigma: float = 0.2,
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dt: float = 1 / 252,
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) -> np.ndarray:
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rng = np.random.default_rng(seed)
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z = rng.standard_normal(n - 1)
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drift = (mu - 0.5 * sigma**2) * dt
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diffusion = sigma * np.sqrt(dt) * z
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log_returns = drift + diffusion
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prices = np.empty(n, dtype=np.float64)
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prices[0] = start_price
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np.cumsum(log_returns, out=prices[1:])
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prices[1:] += np.log(start_price)
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np.exp(prices[1:], out=prices[1:])
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prices[0] = start_price
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return prices
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CLOSE = generate_gbm(N)
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OPEN = np.roll(CLOSE, 1)
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OPEN[0] = CLOSE[0]
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HIGH = np.maximum(OPEN, CLOSE) + 0.1
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LOW = np.minimum(OPEN, CLOSE) - 0.1
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VOLUME = np.linspace(1_000.0, 2_000.0, N)
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S_CLOSE = pd.Series(CLOSE, name="close")
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S_OPEN = pd.Series(OPEN, name="open")
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S_HIGH = pd.Series(HIGH, name="high")
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S_LOW = pd.Series(LOW, name="low")
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S_VOLUME = pd.Series(VOLUME, name="volume")
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SPECIAL_PTA: dict[str, Callable[[], np.ndarray]] = {
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"cmo": lambda: ta.cmo(S_CLOSE, length=14, talib=False).to_numpy(),
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"apo": lambda: ta.apo(S_CLOSE, fast=12, slow=26, mamode="ema", talib=False).to_numpy(),
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"cfo": lambda: (100.0 * (S_CLOSE - ta.linreg(S_CLOSE, length=14, tsf=False, talib=False)) / S_CLOSE).to_numpy(),
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"trix": lambda: ta.trix(S_CLOSE, length=18).iloc[:, 0].to_numpy(),
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"dpo": lambda: ta.dpo(S_CLOSE, length=20, centered=False).to_numpy(),
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}
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ALIASES = {
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"medprice": "midprice",
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"typprice": "hlc3",
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"avgprice": "ohlc4",
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"midbody": "mid_body",
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"mom": "momentum",
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"bbands": "bbands",
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"stddev": "stdev",
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"zscore": "zscore",
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"tr": "true_range",
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"ema_alpha": None,
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"dema_alpha": None,
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}
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SKIP_PRIVATE = {
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"_arr",
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"_ptr",
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"_out",
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"_offset",
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"_wrap",
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"_wrap_multi",
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"_pa",
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"_pg",
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"_pg2",
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"_pf",
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}
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def normalize_pta_output(v: Any) -> np.ndarray:
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if isinstance(v, pd.Series):
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return v.to_numpy()
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if isinstance(v, pd.DataFrame):
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# default: first numeric column
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return v.iloc[:, 0].to_numpy()
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if isinstance(v, tuple):
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if len(v) == 0:
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return np.array([], dtype=np.float64)
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return np.asarray(v[0], dtype=np.float64)
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return np.asarray(v, dtype=np.float64)
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def get_q_functions() -> dict[str, Callable[..., Any]]:
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out: dict[str, Callable[..., Any]] = {}
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for name, fn in inspect.getmembers(q, inspect.isfunction):
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if name.startswith("_") or name in SKIP_PRIVATE:
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continue
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out[name] = fn
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return out
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def choose_pta_name(q_name: str) -> str | None:
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if q_name in ALIASES:
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return ALIASES[q_name]
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if hasattr(ta, q_name):
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return q_name
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return None
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def call_q(name: str, fn: Callable[..., Any]) -> np.ndarray:
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# conservative defaults based on function signature
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sig = inspect.signature(fn)
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params = [
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n
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for n, p in sig.parameters.items()
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if p.kind not in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
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]
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kwargs: dict[str, Any] = {}
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# shared defaults
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if "length" in params:
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kwargs["length"] = sig.parameters["length"].default if sig.parameters["length"].default is not inspect._empty else 14
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if "fast" in params:
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kwargs["fast"] = 12
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if "slow" in params:
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kwargs["slow"] = 26
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if "signal" in params:
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kwargs["signal"] = 9
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if "offset" in params:
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kwargs["offset"] = 0
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# positional construction by semantic names
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args: list[Any] = []
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for p in params:
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if p in kwargs:
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continue
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if p == "close":
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args.append(CLOSE)
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elif p == "open":
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args.append(OPEN)
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elif p == "high":
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args.append(HIGH)
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elif p == "low":
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args.append(LOW)
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elif p == "volume":
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args.append(VOLUME)
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elif p == "x":
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args.append(CLOSE)
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elif p == "y":
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args.append(np.roll(CLOSE, 3))
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elif p == "actual":
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args.append(CLOSE)
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elif p == "predicted":
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args.append(np.roll(CLOSE, 1))
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elif p in {"kernel", "lengths"}:
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# unsupported generics in all-indicator sweep
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raise RuntimeError(f"unsupported arg {p} in generic sweep")
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else:
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# keep default when available
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param = sig.parameters[p]
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if param.default is inspect._empty:
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raise RuntimeError(f"required arg {p} not mapped")
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out = fn(*args, **kwargs)
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return normalize_pta_output(out)
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def call_pta(q_name: str) -> np.ndarray:
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if q_name in SPECIAL_PTA:
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return SPECIAL_PTA[q_name]()
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pta_name = choose_pta_name(q_name)
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if not pta_name:
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raise RuntimeError("no pandas-ta mapping")
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pta_fn = getattr(ta, pta_name)
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sig = inspect.signature(pta_fn)
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params = [
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n
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for n, p in sig.parameters.items()
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if p.kind not in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
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]
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kwargs: dict[str, Any] = {}
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if "length" in params:
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kwargs["length"] = 14
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if "fast" in params:
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kwargs["fast"] = 12
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if "slow" in params:
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kwargs["slow"] = 26
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if "signal" in params:
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kwargs["signal"] = 9
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if "offset" in params:
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kwargs["offset"] = 0
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args: list[Any] = []
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for p in params:
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if p in kwargs:
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continue
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if p == "close":
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args.append(S_CLOSE)
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elif p == "open":
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args.append(S_OPEN)
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elif p == "high":
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args.append(S_HIGH)
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elif p == "low":
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args.append(S_LOW)
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elif p == "volume":
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args.append(S_VOLUME)
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elif p in {"x", "seriesX"}:
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args.append(S_CLOSE)
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elif p in {"y", "seriesY"}:
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args.append(pd.Series(np.roll(CLOSE, 3)))
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elif p == "mamode":
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kwargs["mamode"] = "ema"
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elif p == "talib":
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kwargs["talib"] = False
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elif p == "centered":
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kwargs["centered"] = False
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elif p == "drift":
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kwargs["drift"] = 1
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elif p == "scalar":
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kwargs["scalar"] = 100
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else:
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# leave defaults for unknown optional args
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pass
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out = pta_fn(*args, **kwargs)
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return normalize_pta_output(out)
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def verify_last_n(qtl_arr: np.ndarray, pta_arr: np.ndarray, tol: float = DEFAULT_TOL) -> tuple[bool, float, int]:
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if len(qtl_arr) != len(pta_arr):
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return False, float("inf"), 0
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start = max(0, len(qtl_arr) - VERIFY_COUNT)
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q_tail = qtl_arr[start:]
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p_tail = pta_arr[start:]
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finite = np.isfinite(q_tail) & np.isfinite(p_tail)
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n = int(np.sum(finite))
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if n == 0:
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return False, float("inf"), 0
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d = np.abs(q_tail[finite] - p_tail[finite])
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md = float(np.max(d))
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return md <= tol, md, n
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def main() -> int:
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funcs = get_q_functions()
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names = sorted(funcs.keys())
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rows: list[tuple[str, str, str]] = []
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ok = 0
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fail = 0
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for name in names:
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fn = funcs[name]
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try:
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qv = call_q(name, fn)
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pv = call_pta(name)
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passed, max_diff, n = verify_last_n(qv, pv, DEFAULT_TOL)
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if passed:
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rows.append((name, "✔️", f"max_diff={max_diff:.3e}, n={n}"))
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ok += 1
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else:
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rows.append((name, "⚠️", f"max_diff={max_diff:.3e}, n={n}"))
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fail += 1
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except Exception as ex: # noqa: BLE001
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rows.append((name, "⚠️", f"{type(ex).__name__}: {ex}"))
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fail += 1
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lines = [
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"# pandas-ta validation sweep across exported Python wrapper indicators",
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"",
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f"- Total indicators scanned: **{len(rows)}**",
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f"- Successful (✔️): **{ok}**",
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f"- Failing (⚠️): **{fail}**",
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"",
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"| Indicator | Status | Notes |",
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"|---|---:|---|",
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]
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lines.extend([f"| `{n}` | {s} | {note} |" for n, s, note in rows])
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REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
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REPORT_PATH.write_text("\n".join(lines), encoding="utf-8")
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print(f"Wrote {REPORT_PATH}")
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print(f"TOTAL={len(rows)} OK={ok} FAIL={fail}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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