Wickra 0.1.0: streaming-first technical indicators
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
This commit is contained in:
kingchenc
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[package]
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name = "wickra-python"
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description = "Python bindings for the Wickra streaming-first technical indicators library."
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version.workspace = true
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authors.workspace = true
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edition.workspace = true
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rust-version.workspace = true
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license.workspace = true
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repository.workspace = true
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homepage.workspace = true
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readme.workspace = true
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keywords.workspace = true
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categories.workspace = true
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publish = false
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[lib]
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name = "_wickra"
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crate-type = ["cdylib"]
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[lints]
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workspace = true
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[dependencies]
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wickra-core = { workspace = true }
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pyo3 = { workspace = true }
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numpy = { workspace = true }
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"""Cross-library benchmark: Wickra vs TA-Lib vs pandas-ta vs talipp vs finta.
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Runs each library through identical batch and streaming workloads, then prints a
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table of timings. Libraries that are not installed are skipped automatically, so
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the script always produces output regardless of the local environment.
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Usage::
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python -m benchmarks.compare_libraries
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python -m benchmarks.compare_libraries --size 50000 --streaming-window 5000
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Notes:
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- "Batch" means computing the indicator over the whole price series in one call,
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which is what classic libraries support.
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- "Streaming" simulates live trading: after seeding with ``streaming_window``
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historical bars, we keep appending one new price and recomputing the latest
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indicator value. Libraries without an incremental API have to recompute the
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whole indicator on every tick; Wickra updates in O(1). This is the gap the
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library was built to expose.
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"""
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from __future__ import annotations
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import argparse
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import contextlib
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import importlib
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import statistics
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import time
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from dataclasses import dataclass
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from typing import Callable, Dict, List, Optional
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import numpy as np
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# --------------------------------------------------------------------------- #
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# Library availability detection
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# --------------------------------------------------------------------------- #
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def _try_import(name: str):
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try:
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return importlib.import_module(name)
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except Exception:
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return None
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TALIB = _try_import("talib")
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PANDAS_TA = _try_import("pandas_ta")
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TALIPP = _try_import("talipp.indicators") or _try_import("talipp")
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FINTA = _try_import("finta")
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PD = _try_import("pandas")
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import wickra as WICKRA # noqa: E402 -- the library under test must be importable
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# --------------------------------------------------------------------------- #
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# Timing helpers
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# --------------------------------------------------------------------------- #
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@dataclass
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class Sample:
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library: str
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indicator: str
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mode: str
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seconds: float
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iterations: int
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@property
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def per_iter_us(self) -> float:
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return (self.seconds / self.iterations) * 1_000_000
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def time_call(fn: Callable[[], None], iterations: int) -> float:
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"""Time ``fn`` over ``iterations`` calls, returning total wall seconds."""
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fn() # one warmup call to populate caches
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start = time.perf_counter()
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for _ in range(iterations):
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fn()
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return time.perf_counter() - start
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def gen_prices(n: int, seed: int = 0xC0FFEE) -> np.ndarray:
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rng = np.random.default_rng(seed)
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walk = rng.standard_normal(n) * 0.4
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return 100.0 + np.cumsum(walk)
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def gen_ohlc(n: int, seed: int = 0xC0FFEE) -> tuple:
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close = gen_prices(n, seed)
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spread = 0.5 + np.abs(np.sin(np.arange(n) * 0.07))
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high = close + spread
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low = close - spread
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volume = np.full(n, 1_000.0)
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return high, low, close, volume
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# --------------------------------------------------------------------------- #
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# Per-library indicator runners. Each returns ``None`` to skip when unavailable.
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# --------------------------------------------------------------------------- #
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def wickra_sma_batch(prices: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.SMA(20).batch(prices)
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def talib_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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return None if TALIB is None else (lambda: TALIB.SMA(prices, timeperiod=20))
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def pandas_ta_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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s = PD.Series(prices)
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return lambda: PANDAS_TA.sma(s, length=20)
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def finta_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": prices, "high": prices, "low": prices, "close": prices, "volume": np.ones_like(prices)})
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return lambda: FINTA.TA.SMA(df, period=20)
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def talipp_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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# talipp's SMA accepts an initial list of values
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from talipp.indicators import SMA # type: ignore
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return lambda: SMA(period=20, input_values=list(prices))
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def wickra_rsi_batch(prices: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.RSI(14).batch(prices)
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def talib_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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return None if TALIB is None else (lambda: TALIB.RSI(prices, timeperiod=14))
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def pandas_ta_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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s = PD.Series(prices)
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return lambda: PANDAS_TA.rsi(s, length=14)
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def finta_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": prices, "high": prices, "low": prices, "close": prices, "volume": np.ones_like(prices)})
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return lambda: FINTA.TA.RSI(df, period=14)
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def talipp_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import RSI # type: ignore
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return lambda: RSI(period=14, input_values=list(prices))
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def wickra_bollinger_batch(prices: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.BollingerBands(20, 2.0).batch(prices)
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def wickra_ema_batch(prices: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.EMA(20).batch(prices)
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def talib_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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return None if TALIB is None else (lambda: TALIB.EMA(prices, timeperiod=20))
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def pandas_ta_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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s = PD.Series(prices)
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return lambda: PANDAS_TA.ema(s, length=20)
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def finta_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": prices, "high": prices, "low": prices, "close": prices, "volume": np.ones_like(prices)})
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return lambda: FINTA.TA.EMA(df, period=20)
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def talipp_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import EMA # type: ignore
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return lambda: EMA(period=20, input_values=list(prices))
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def wickra_macd_batch(prices: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.MACD().batch(prices)
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def talib_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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return None if TALIB is None else (lambda: TALIB.MACD(prices))
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def pandas_ta_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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s = PD.Series(prices)
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return lambda: PANDAS_TA.macd(s)
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def finta_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": prices, "high": prices, "low": prices, "close": prices, "volume": np.ones_like(prices)})
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return lambda: FINTA.TA.MACD(df)
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def talipp_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import MACD # type: ignore
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return lambda: MACD(fast_period=12, slow_period=26, signal_period=9, input_values=list(prices))
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def wickra_atr_batch(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> Callable[[], None]:
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return lambda: WICKRA.ATR(14).batch(high, low, close)
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def talib_atr_batch(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> Optional[Callable[[], None]]:
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return None if TALIB is None else (lambda: TALIB.ATR(high, low, close, timeperiod=14))
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def finta_atr_batch(_high: np.ndarray, _low: np.ndarray, _close: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": _close, "high": _high, "low": _low, "close": _close, "volume": np.ones_like(_close)})
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return lambda: FINTA.TA.ATR(df, period=14)
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def talipp_atr_batch(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import ATR # type: ignore
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from talipp.ohlcv import OHLCV
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bars = [OHLCV(open=c, high=h, low=l, close=c, volume=1.0, time=i) for i, (h, l, c) in enumerate(zip(high, low, close))]
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return lambda: ATR(period=14, input_values=bars)
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def talib_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIB is None:
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return None
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return lambda: TALIB.BBANDS(prices, timeperiod=20, nbdevup=2, nbdevdn=2)
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def pandas_ta_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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s = PD.Series(prices)
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return lambda: PANDAS_TA.bbands(s, length=20, std=2.0)
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def finta_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if FINTA is None or PD is None:
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return None
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df = PD.DataFrame({"open": prices, "high": prices, "low": prices, "close": prices, "volume": np.ones_like(prices)})
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return lambda: FINTA.TA.BBANDS(df, period=20, std_multiplier=2.0)
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def talipp_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import BB # type: ignore
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return lambda: BB(period=20, std_dev_mult=2.0, input_values=list(prices))
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# --------------------------------------------------------------------------- #
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# Streaming scenario: per-tick latency
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# --------------------------------------------------------------------------- #
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def wickra_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
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def run() -> None:
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rsi = WICKRA.RSI(14)
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rsi.batch(seed) # warm up
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for p in live:
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rsi.update(float(p))
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return run
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def talib_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIB is None:
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return None
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def run() -> None:
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history = list(seed)
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for p in live:
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history.append(float(p))
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TALIB.RSI(np.asarray(history), timeperiod=14)
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return run
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def pandas_ta_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
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if PANDAS_TA is None or PD is None:
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return None
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def run() -> None:
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history = list(seed)
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for p in live:
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history.append(float(p))
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PANDAS_TA.rsi(PD.Series(history), length=14)
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return run
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def talipp_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
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if TALIPP is None:
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return None
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from talipp.indicators import RSI # type: ignore
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def run() -> None:
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rsi = RSI(period=14, input_values=list(seed))
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for p in live:
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rsi.add(float(p))
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return run
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# --------------------------------------------------------------------------- #
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# Runner
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# --------------------------------------------------------------------------- #
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BATCH_INDICATORS = [
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("SMA(20)", [
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("Wickra", wickra_sma_batch),
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("TA-Lib", talib_sma_batch),
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("pandas-ta", pandas_ta_sma_batch),
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("finta", finta_sma_batch),
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("talipp", talipp_sma_batch),
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]),
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("EMA(20)", [
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("Wickra", wickra_ema_batch),
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("TA-Lib", talib_ema_batch),
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("pandas-ta", pandas_ta_ema_batch),
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("finta", finta_ema_batch),
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("talipp", talipp_ema_batch),
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]),
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("RSI(14)", [
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("Wickra", wickra_rsi_batch),
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("TA-Lib", talib_rsi_batch),
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("pandas-ta", pandas_ta_rsi_batch),
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("finta", finta_rsi_batch),
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("talipp", talipp_rsi_batch),
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]),
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("MACD(12, 26, 9)", [
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("Wickra", wickra_macd_batch),
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("TA-Lib", talib_macd_batch),
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("pandas-ta", pandas_ta_macd_batch),
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("finta", finta_macd_batch),
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("talipp", talipp_macd_batch),
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]),
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("Bollinger(20, 2.0)", [
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("Wickra", wickra_bollinger_batch),
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("TA-Lib", talib_bollinger_batch),
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("pandas-ta", pandas_ta_bollinger_batch),
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("finta", finta_bollinger_batch),
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("talipp", talipp_bollinger_batch),
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]),
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]
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OHLC_INDICATORS = [
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("ATR(14)", [
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("Wickra", wickra_atr_batch),
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("TA-Lib", talib_atr_batch),
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("finta", finta_atr_batch),
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("talipp", talipp_atr_batch),
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]),
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]
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STREAMING_INDICATORS = [
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("RSI(14)", [
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("Wickra", wickra_rsi_streaming),
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("TA-Lib", talib_rsi_streaming),
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("pandas-ta", pandas_ta_rsi_streaming),
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("talipp", talipp_rsi_streaming),
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]),
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]
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def run_batch(prices: np.ndarray, iterations: int) -> List[Sample]:
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out: List[Sample] = []
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for indicator_name, libs in BATCH_INDICATORS:
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for lib_name, factory in libs:
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runner = factory(prices)
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if runner is None:
|
||||
continue
|
||||
secs = time_call(runner, iterations)
|
||||
out.append(Sample(lib_name, indicator_name, "batch", secs, iterations))
|
||||
return out
|
||||
|
||||
|
||||
def run_ohlc(
|
||||
high: np.ndarray,
|
||||
low: np.ndarray,
|
||||
close: np.ndarray,
|
||||
iterations: int,
|
||||
) -> List[Sample]:
|
||||
out: List[Sample] = []
|
||||
for indicator_name, libs in OHLC_INDICATORS:
|
||||
for lib_name, factory in libs:
|
||||
runner = factory(high, low, close)
|
||||
if runner is None:
|
||||
continue
|
||||
secs = time_call(runner, iterations)
|
||||
out.append(Sample(lib_name, indicator_name, "batch", secs, iterations))
|
||||
return out
|
||||
|
||||
|
||||
def run_streaming(prices: np.ndarray, streaming_window: int, iterations: int) -> List[Sample]:
|
||||
out: List[Sample] = []
|
||||
seed = prices[:streaming_window]
|
||||
live = prices[streaming_window:]
|
||||
if len(live) == 0:
|
||||
return out
|
||||
for indicator_name, libs in STREAMING_INDICATORS:
|
||||
for lib_name, factory in libs:
|
||||
runner = factory(seed, live)
|
||||
if runner is None:
|
||||
continue
|
||||
secs = time_call(runner, iterations)
|
||||
sample = Sample(lib_name, indicator_name, "streaming", secs, iterations)
|
||||
sample.iterations = iterations * len(live) # per-tick normalization
|
||||
out.append(sample)
|
||||
return out
|
||||
|
||||
|
||||
def render_table(rows: List[Sample]) -> str:
|
||||
if not rows:
|
||||
return "(no results)"
|
||||
grouped: Dict[str, List[Sample]] = {}
|
||||
for r in rows:
|
||||
key = f"{r.mode} | {r.indicator}"
|
||||
grouped.setdefault(key, []).append(r)
|
||||
|
||||
lines: List[str] = []
|
||||
lines.append("")
|
||||
lines.append("Reading the tables: lower µs/op = faster. The 'vs Wickra' column says")
|
||||
lines.append("how many times slower (or faster) the other library is compared to Wickra.")
|
||||
for key, samples in grouped.items():
|
||||
baseline = next((s for s in samples if s.library == "Wickra"), samples[0])
|
||||
base = baseline.per_iter_us
|
||||
lines.append("")
|
||||
lines.append(key)
|
||||
lines.append("-" * len(key))
|
||||
lines.append(
|
||||
f"{'library':<14} {'µs/op':>14} {'vs Wickra':>22} {'verdict':<10}"
|
||||
)
|
||||
winner = min(samples, key=lambda x: x.per_iter_us)
|
||||
for s in sorted(samples, key=lambda x: x.per_iter_us):
|
||||
ratio = s.per_iter_us / base if base > 0 else float("nan")
|
||||
if s.library == "Wickra":
|
||||
comparison = "(reference)"
|
||||
elif s.per_iter_us > base:
|
||||
comparison = f"{ratio:>5.2f}x slower"
|
||||
else:
|
||||
comparison = f"{base / s.per_iter_us:>5.2f}x faster"
|
||||
verdict = "★ winner" if s is winner else ""
|
||||
lines.append(
|
||||
f"{s.library:<14} {s.per_iter_us:>14.3f} {comparison:>22} {verdict:<10}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__.splitlines()[0] if __doc__ else None)
|
||||
parser.add_argument("--size", type=int, default=20_000, help="number of prices")
|
||||
parser.add_argument("--iterations", type=int, default=20, help="batch repetitions per timing")
|
||||
parser.add_argument(
|
||||
"--streaming-window",
|
||||
type=int,
|
||||
default=5_000,
|
||||
help="number of historical prices to seed before the live ticks begin",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--streaming-iterations",
|
||||
type=int,
|
||||
default=3,
|
||||
help="repetitions of the streaming workload (each iteration replays all live ticks)",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
prices = gen_prices(args.size)
|
||||
|
||||
available = []
|
||||
if TALIB is not None: available.append("TA-Lib")
|
||||
if PANDAS_TA is not None: available.append("pandas-ta")
|
||||
if FINTA is not None: available.append("finta")
|
||||
if TALIPP is not None: available.append("talipp")
|
||||
print(f"Wickra benchmark suite — wickra=v{WICKRA.__version__}")
|
||||
print(f"Comparing against: {', '.join(available) if available else '(no peer libraries installed; install [bench] extra)'}")
|
||||
print(f"Series length: {args.size} • batch iterations: {args.iterations}")
|
||||
print(f"Streaming window: {args.streaming_window} seed, {args.size - args.streaming_window} live")
|
||||
|
||||
high, low, close, _ = gen_ohlc(args.size)
|
||||
batch_rows = run_batch(prices, args.iterations)
|
||||
ohlc_rows = run_ohlc(high, low, close, args.iterations)
|
||||
streaming_rows = run_streaming(prices, args.streaming_window, args.streaming_iterations)
|
||||
|
||||
print(render_table(batch_rows + ohlc_rows + streaming_rows))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,62 @@
|
||||
[build-system]
|
||||
requires = ["maturin>=1.7,<2.0"]
|
||||
build-backend = "maturin"
|
||||
|
||||
[project]
|
||||
name = "wickra"
|
||||
version = "0.1.0"
|
||||
description = "Streaming-first technical indicators: incremental, fast, install-free."
|
||||
readme = "../../README.md"
|
||||
license = { text = "Apache-2.0" }
|
||||
requires-python = ">=3.9"
|
||||
keywords = ["finance", "trading", "indicators", "technical-analysis", "ta-lib"]
|
||||
classifiers = [
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Financial and Insurance Industry",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3 :: Only",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
"Programming Language :: Python :: 3.12",
|
||||
"Programming Language :: Rust",
|
||||
"Topic :: Office/Business :: Financial :: Investment",
|
||||
"Topic :: Scientific/Engineering :: Mathematics",
|
||||
]
|
||||
dependencies = [
|
||||
"numpy>=1.22",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
test = [
|
||||
"pytest>=7",
|
||||
"numpy>=1.22",
|
||||
"hypothesis>=6",
|
||||
]
|
||||
bench = [
|
||||
"pytest-benchmark>=4",
|
||||
"TA-Lib; platform_system != 'Windows'",
|
||||
"pandas-ta>=0.3.14b",
|
||||
"talipp>=2",
|
||||
"finta>=1.3",
|
||||
"pandas>=2",
|
||||
"numpy>=1.22",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://github.com/wickra/wickra"
|
||||
Repository = "https://github.com/wickra/wickra"
|
||||
Issues = "https://github.com/wickra/wickra/issues"
|
||||
|
||||
[tool.maturin]
|
||||
manifest-path = "Cargo.toml"
|
||||
python-source = "python"
|
||||
module-name = "wickra._wickra"
|
||||
features = ["pyo3/extension-module"]
|
||||
strip = true
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
addopts = "-ra -q"
|
||||
filterwarnings = ["error"]
|
||||
@@ -0,0 +1,82 @@
|
||||
"""Wickra: streaming-first technical indicators.
|
||||
|
||||
Every indicator is available both in streaming mode (call ``update(value)`` per
|
||||
new data point) and batch mode (call ``batch(numpy_array)`` over a full series).
|
||||
Warmup positions in batch output are returned as ``NaN`` so the shape always
|
||||
matches the input.
|
||||
|
||||
Example::
|
||||
|
||||
import numpy as np
|
||||
import wickra as ta
|
||||
|
||||
prices = np.linspace(100, 200, 1000)
|
||||
rsi = ta.RSI(14)
|
||||
values = rsi.batch(prices) # numpy array, NaN during warmup
|
||||
|
||||
# Or streaming:
|
||||
rsi = ta.RSI(14)
|
||||
for p in prices:
|
||||
v = rsi.update(p) # None during warmup, then float
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from ._wickra import (
|
||||
__version__,
|
||||
ADX,
|
||||
ATR,
|
||||
Aroon,
|
||||
AwesomeOscillator,
|
||||
BollingerBands,
|
||||
CCI,
|
||||
DEMA,
|
||||
Donchian,
|
||||
EMA,
|
||||
HMA,
|
||||
KAMA,
|
||||
Keltner,
|
||||
MACD,
|
||||
MFI,
|
||||
OBV,
|
||||
PSAR,
|
||||
ROC,
|
||||
RSI,
|
||||
SMA,
|
||||
Stochastic,
|
||||
TEMA,
|
||||
TRIX,
|
||||
VWAP,
|
||||
WilliamsR,
|
||||
WMA,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"__version__",
|
||||
"SMA",
|
||||
"EMA",
|
||||
"WMA",
|
||||
"RSI",
|
||||
"MACD",
|
||||
"BollingerBands",
|
||||
"ATR",
|
||||
"Stochastic",
|
||||
"OBV",
|
||||
"DEMA",
|
||||
"TEMA",
|
||||
"HMA",
|
||||
"KAMA",
|
||||
"CCI",
|
||||
"ROC",
|
||||
"WilliamsR",
|
||||
"ADX",
|
||||
"MFI",
|
||||
"TRIX",
|
||||
"PSAR",
|
||||
"Keltner",
|
||||
"Donchian",
|
||||
"VWAP",
|
||||
"AwesomeOscillator",
|
||||
"Aroon",
|
||||
]
|
||||
@@ -0,0 +1,137 @@
|
||||
"""Type stubs for the Wickra public API."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Mapping, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
__version__: str
|
||||
|
||||
CandleLike = Union[
|
||||
Tuple[float, float, float, float, float, int],
|
||||
Mapping[str, Any],
|
||||
]
|
||||
|
||||
class SMA:
|
||||
def __init__(self, period: int) -> None: ...
|
||||
def update(self, value: float) -> Optional[float]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
@property
|
||||
def value(self) -> Optional[float]: ...
|
||||
|
||||
class EMA:
|
||||
def __init__(self, period: int) -> None: ...
|
||||
def update(self, value: float) -> Optional[float]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
@property
|
||||
def alpha(self) -> float: ...
|
||||
@property
|
||||
def value(self) -> Optional[float]: ...
|
||||
|
||||
class WMA:
|
||||
def __init__(self, period: int) -> None: ...
|
||||
def update(self, value: float) -> Optional[float]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
@property
|
||||
def value(self) -> Optional[float]: ...
|
||||
|
||||
class RSI:
|
||||
def __init__(self, period: int = 14) -> None: ...
|
||||
def update(self, value: float) -> Optional[float]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
@property
|
||||
def value(self) -> Optional[float]: ...
|
||||
|
||||
class MACD:
|
||||
def __init__(self, fast: int = 12, slow: int = 26, signal: int = 9) -> None: ...
|
||||
def update(self, value: float) -> Optional[Tuple[float, float, float]]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
"""Returns shape ``(n, 3)`` with columns ``[macd, signal, histogram]``. NaN during warmup."""
|
||||
...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def periods(self) -> Tuple[int, int, int]: ...
|
||||
|
||||
class BollingerBands:
|
||||
def __init__(self, period: int = 20, multiplier: float = 2.0) -> None: ...
|
||||
def update(self, value: float) -> Optional[Tuple[float, float, float, float]]: ...
|
||||
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
"""Returns shape ``(n, 4)`` with columns ``[upper, middle, lower, stddev]``."""
|
||||
...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
@property
|
||||
def multiplier(self) -> float: ...
|
||||
|
||||
class ATR:
|
||||
def __init__(self, period: int = 14) -> None: ...
|
||||
def update(self, candle: CandleLike) -> Optional[float]: ...
|
||||
def batch(
|
||||
self,
|
||||
high: NDArray[np.float64],
|
||||
low: NDArray[np.float64],
|
||||
close: NDArray[np.float64],
|
||||
) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def period(self) -> int: ...
|
||||
|
||||
class Stochastic:
|
||||
def __init__(self, k_period: int = 14, d_period: int = 3) -> None: ...
|
||||
def update(self, candle: CandleLike) -> Optional[Tuple[float, float]]: ...
|
||||
def batch(
|
||||
self,
|
||||
high: NDArray[np.float64],
|
||||
low: NDArray[np.float64],
|
||||
close: NDArray[np.float64],
|
||||
) -> NDArray[np.float64]:
|
||||
"""Returns shape ``(n, 2)`` with columns ``[k, d]``."""
|
||||
...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def periods(self) -> Tuple[int, int]: ...
|
||||
|
||||
class OBV:
|
||||
def __init__(self) -> None: ...
|
||||
def update(self, candle: CandleLike) -> Optional[float]: ...
|
||||
def batch(
|
||||
self,
|
||||
close: NDArray[np.float64],
|
||||
volume: NDArray[np.float64],
|
||||
) -> NDArray[np.float64]: ...
|
||||
def reset(self) -> None: ...
|
||||
def is_ready(self) -> bool: ...
|
||||
def warmup_period(self) -> int: ...
|
||||
@property
|
||||
def value(self) -> Optional[float]: ...
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,36 @@
|
||||
"""Shared pytest fixtures for the Wickra Python test suite."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def linear_prices() -> np.ndarray:
|
||||
"""Strictly increasing prices: 1, 2, 3, ..., 50."""
|
||||
return np.arange(1.0, 51.0, dtype=np.float64)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def constant_prices() -> np.ndarray:
|
||||
"""50 prices of 100.0."""
|
||||
return np.full(50, 100.0, dtype=np.float64)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sine_prices() -> np.ndarray:
|
||||
"""Smooth sine-wave prices used to stress the indicators a little."""
|
||||
t = np.arange(200, dtype=np.float64)
|
||||
return 50.0 + 10.0 * np.sin(t * 0.13) + 4.0 * np.cos(t * 0.41)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def ohlc_series() -> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
"""Synthetic high / low / close triple."""
|
||||
t = np.arange(200, dtype=np.float64)
|
||||
close = 100.0 + np.sin(t * 0.15) * 8.0 + np.cos(t * 0.32) * 3.0
|
||||
spread = 0.5 + np.abs(np.sin(t * 0.07))
|
||||
high = close + spread
|
||||
low = close - spread
|
||||
return high, low, close
|
||||
@@ -0,0 +1,114 @@
|
||||
"""Reference-value tests that pin numerical behaviour from the Python side."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import wickra as ta
|
||||
|
||||
|
||||
def test_sma_constant_series():
|
||||
out = ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64))
|
||||
# First 4 are warmup -> NaN; rest equal 42.
|
||||
assert np.all(np.isnan(out[:4]))
|
||||
assert np.allclose(out[4:], 42.0)
|
||||
|
||||
|
||||
def test_sma_known_window():
|
||||
# SMA(3) of [2, 4, 6, 8, 10] -> [_, _, 4, 6, 8]
|
||||
out = ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0]))
|
||||
assert math.isnan(out[0]) and math.isnan(out[1])
|
||||
np.testing.assert_allclose(out[2:], [4.0, 6.0, 8.0])
|
||||
|
||||
|
||||
def test_ema_seed_equals_simple_mean_of_first_window():
|
||||
# EMA(5) seed = mean([10, 20, 30, 40, 50]) = 30
|
||||
out = ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0]))
|
||||
assert math.isnan(out[0])
|
||||
assert math.isclose(out[4], 30.0, abs_tol=1e-12)
|
||||
|
||||
|
||||
def test_wma_known_window():
|
||||
# WMA(4) of [1, 2, 3, 4] = (1*1 + 2*2 + 3*3 + 4*4)/10 = 3
|
||||
out = ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0]))
|
||||
assert math.isnan(out[0]) and math.isnan(out[1]) and math.isnan(out[2])
|
||||
assert math.isclose(out[3], 3.0, abs_tol=1e-12)
|
||||
|
||||
|
||||
def test_rsi_pure_uptrend_is_100():
|
||||
out = ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64))
|
||||
np.testing.assert_allclose(out[14:], 100.0)
|
||||
|
||||
|
||||
def test_rsi_pure_downtrend_is_0():
|
||||
out = ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0))
|
||||
np.testing.assert_allclose(out[14:], 0.0)
|
||||
|
||||
|
||||
def test_rsi_flat_series_is_50():
|
||||
out = ta.RSI(14).batch(np.full(30, 100.0))
|
||||
np.testing.assert_allclose(out[14:], 50.0)
|
||||
|
||||
|
||||
def test_rsi_wilder_textbook_first_value():
|
||||
"""Wilder's original 14-period example, ~70.46 at the first emit."""
|
||||
prices = np.array(
|
||||
[
|
||||
44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08,
|
||||
45.89, 46.03, 45.61, 46.28, 46.28,
|
||||
],
|
||||
dtype=np.float64,
|
||||
)
|
||||
out = ta.RSI(14).batch(prices)
|
||||
assert math.isclose(out[14], 70.464, abs_tol=0.05)
|
||||
|
||||
|
||||
def test_macd_constant_series_converges_to_zero():
|
||||
out = ta.MACD().batch(np.full(200, 100.0))
|
||||
# Last row's MACD and signal must be ~0.
|
||||
last = out[-1]
|
||||
assert math.isclose(last[0], 0.0, abs_tol=1e-9)
|
||||
assert math.isclose(last[1], 0.0, abs_tol=1e-9)
|
||||
assert math.isclose(last[2], 0.0, abs_tol=1e-9)
|
||||
|
||||
|
||||
def test_bollinger_constant_series_zero_width():
|
||||
out = ta.BollingerBands(20, 2.0).batch(np.full(50, 100.0))
|
||||
row = out[-1]
|
||||
np.testing.assert_allclose(row, [100.0, 100.0, 100.0, 0.0], atol=1e-12)
|
||||
|
||||
|
||||
def test_bollinger_upper_middle_lower_ordering():
|
||||
out = ta.BollingerBands(20, 2.0).batch(np.linspace(50.0, 150.0, 100))
|
||||
ready = out[~np.isnan(out[:, 0])]
|
||||
assert np.all(ready[:, 0] >= ready[:, 1])
|
||||
assert np.all(ready[:, 1] >= ready[:, 2])
|
||||
assert np.all(ready[:, 3] >= 0.0)
|
||||
|
||||
|
||||
def test_atr_constant_range_constant_output():
|
||||
high = np.full(30, 11.0)
|
||||
low = np.full(30, 9.0)
|
||||
close = np.full(30, 10.0)
|
||||
out = ta.ATR(14).batch(high, low, close)
|
||||
# Once seeded, ATR equals the constant TR of 2.
|
||||
np.testing.assert_allclose(out[13:], 2.0, atol=1e-12)
|
||||
|
||||
|
||||
def test_stochastic_extremes():
|
||||
# Close at the top of a 3-period range -> %K = 100.
|
||||
high = np.array([10.0, 11.0, 12.0])
|
||||
low = np.array([8.0, 9.0, 10.0])
|
||||
close = np.array([9.0, 10.0, 12.0])
|
||||
out = ta.Stochastic(3, 1).batch(high, low, close)
|
||||
assert math.isclose(out[2, 0], 100.0, abs_tol=1e-12)
|
||||
|
||||
|
||||
def test_obv_cumulative_known_sequence():
|
||||
close = np.array([10.0, 11.0, 10.5, 10.5, 12.0])
|
||||
volume = np.array([100.0, 20.0, 30.0, 40.0, 10.0])
|
||||
out = ta.OBV().batch(close, volume)
|
||||
np.testing.assert_allclose(out, [0.0, 20.0, -10.0, -10.0, 0.0])
|
||||
@@ -0,0 +1,88 @@
|
||||
"""Tests for the indicator lifecycle methods: reset, is_ready, warmup_period, repr."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import wickra as ta
|
||||
|
||||
SCALAR_INDICATORS = [
|
||||
(ta.SMA, (14,)),
|
||||
(ta.EMA, (14,)),
|
||||
(ta.WMA, (14,)),
|
||||
(ta.RSI, (14,)),
|
||||
(ta.MACD, ()),
|
||||
(ta.BollingerBands, ()),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
|
||||
def test_is_ready_transitions_after_warmup(cls, args):
|
||||
ind = cls(*args)
|
||||
assert not ind.is_ready()
|
||||
series = np.linspace(1.0, 200.0, 200)
|
||||
ind.batch(series)
|
||||
assert ind.is_ready()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
|
||||
def test_reset_returns_to_initial_state(cls, args):
|
||||
ind = cls(*args)
|
||||
ind.batch(np.linspace(1.0, 200.0, 200))
|
||||
assert ind.is_ready()
|
||||
ind.reset()
|
||||
assert not ind.is_ready()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"cls, args, period",
|
||||
[
|
||||
(ta.SMA, (14,), 14),
|
||||
(ta.EMA, (14,), 14),
|
||||
(ta.WMA, (14,), 14),
|
||||
(ta.RSI, (14,), 15),
|
||||
(ta.BollingerBands, (20, 2.0), 20),
|
||||
],
|
||||
)
|
||||
def test_warmup_period(cls, args, period):
|
||||
assert cls(*args).warmup_period() == period
|
||||
|
||||
|
||||
def test_repr_contains_class_and_parameters():
|
||||
assert "SMA" in repr(ta.SMA(14))
|
||||
assert "14" in repr(ta.SMA(14))
|
||||
assert "BollingerBands" in repr(ta.BollingerBands(20, 2.0))
|
||||
|
||||
|
||||
def test_constructor_rejects_zero_period():
|
||||
with pytest.raises(ValueError):
|
||||
ta.SMA(0)
|
||||
with pytest.raises(ValueError):
|
||||
ta.RSI(0)
|
||||
|
||||
|
||||
def test_macd_rejects_fast_geq_slow():
|
||||
with pytest.raises(ValueError):
|
||||
ta.MACD(fast=26, slow=12, signal=9)
|
||||
|
||||
|
||||
def test_bollinger_rejects_non_positive_multiplier():
|
||||
with pytest.raises(ValueError):
|
||||
ta.BollingerBands(20, 0.0)
|
||||
with pytest.raises(ValueError):
|
||||
ta.BollingerBands(20, -1.0)
|
||||
|
||||
|
||||
def test_candle_dict_input_supported():
|
||||
atr = ta.ATR(2)
|
||||
atr.update({"open": 10.0, "high": 11.0, "low": 9.0, "close": 10.5, "volume": 1.0})
|
||||
v = atr.update({"open": 10.5, "high": 12.0, "low": 10.0, "close": 11.0, "volume": 1.0})
|
||||
assert v is not None
|
||||
|
||||
|
||||
def test_candle_tuple_input_supported():
|
||||
atr = ta.ATR(2)
|
||||
atr.update((10.0, 11.0, 9.0, 10.5, 1.0, 0))
|
||||
v = atr.update((10.5, 12.0, 10.0, 11.0, 1.0, 1))
|
||||
assert v is not None
|
||||
@@ -0,0 +1,57 @@
|
||||
"""Smoke tests: every public class can be constructed and emits the right shape."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import wickra as ta
|
||||
|
||||
|
||||
def test_version_is_a_nonempty_string():
|
||||
assert isinstance(ta.__version__, str)
|
||||
assert ta.__version__
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"cls, args",
|
||||
[
|
||||
(ta.SMA, (14,)),
|
||||
(ta.EMA, (14,)),
|
||||
(ta.WMA, (14,)),
|
||||
(ta.RSI, (14,)),
|
||||
],
|
||||
)
|
||||
def test_scalar_batch_returns_same_length(cls, args, sine_prices):
|
||||
out = cls(*args).batch(sine_prices)
|
||||
assert out.shape == sine_prices.shape
|
||||
assert out.dtype == np.float64
|
||||
|
||||
|
||||
def test_macd_batch_returns_n_by_3(sine_prices):
|
||||
out = ta.MACD().batch(sine_prices)
|
||||
assert out.shape == (sine_prices.size, 3)
|
||||
|
||||
|
||||
def test_bollinger_batch_returns_n_by_4(sine_prices):
|
||||
out = ta.BollingerBands().batch(sine_prices)
|
||||
assert out.shape == (sine_prices.size, 4)
|
||||
|
||||
|
||||
def test_atr_batch_shape(ohlc_series):
|
||||
high, low, close = ohlc_series
|
||||
out = ta.ATR(14).batch(high, low, close)
|
||||
assert out.shape == close.shape
|
||||
|
||||
|
||||
def test_stochastic_batch_shape(ohlc_series):
|
||||
high, low, close = ohlc_series
|
||||
out = ta.Stochastic(14, 3).batch(high, low, close)
|
||||
assert out.shape == (close.size, 2)
|
||||
|
||||
|
||||
def test_obv_batch_shape(ohlc_series):
|
||||
_, _, close = ohlc_series
|
||||
volume = np.ones_like(close)
|
||||
out = ta.OBV().batch(close, volume)
|
||||
assert out.shape == close.shape
|
||||
@@ -0,0 +1,117 @@
|
||||
"""For every indicator, batch(prices) must equal repeated update(price).
|
||||
|
||||
This is the central correctness contract of Wickra: the two APIs share one
|
||||
implementation, so they cannot disagree. These tests verify it from Python
|
||||
across the entire warmup → steady-state transition.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import wickra as ta
|
||||
|
||||
|
||||
def _equal_with_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
|
||||
"""NumPy ``==`` treats NaN as not-equal; emulate ``equal_nan`` for floats."""
|
||||
if a.shape != b.shape:
|
||||
return False
|
||||
both_nan = np.isnan(a) & np.isnan(b)
|
||||
diff_ok = np.where(both_nan, 0.0, np.abs(a - b))
|
||||
return bool(np.all(diff_ok <= tol))
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"cls, args",
|
||||
[
|
||||
(ta.SMA, (14,)),
|
||||
(ta.EMA, (14,)),
|
||||
(ta.WMA, (14,)),
|
||||
(ta.RSI, (14,)),
|
||||
],
|
||||
)
|
||||
def test_scalar_streaming_matches_batch(cls, args, sine_prices):
|
||||
batch = cls(*args).batch(sine_prices)
|
||||
|
||||
streamer = cls(*args)
|
||||
streamed = np.array(
|
||||
[streamer.update(float(p)) if streamer is not None else None for p in sine_prices],
|
||||
dtype=object,
|
||||
)
|
||||
# Map None -> NaN to compare against batch.
|
||||
streamed = np.array(
|
||||
[math.nan if v is None else float(v) for v in streamed], dtype=np.float64
|
||||
)
|
||||
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_macd_streaming_matches_batch(sine_prices):
|
||||
batch = ta.MACD().batch(sine_prices)
|
||||
|
||||
streamer = ta.MACD()
|
||||
rows = []
|
||||
for p in sine_prices:
|
||||
v = streamer.update(float(p))
|
||||
if v is None:
|
||||
rows.append([math.nan, math.nan, math.nan])
|
||||
else:
|
||||
rows.append(list(v))
|
||||
streamed = np.array(rows, dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_bollinger_streaming_matches_batch(sine_prices):
|
||||
batch = ta.BollingerBands().batch(sine_prices)
|
||||
|
||||
streamer = ta.BollingerBands()
|
||||
rows = []
|
||||
for p in sine_prices:
|
||||
v = streamer.update(float(p))
|
||||
if v is None:
|
||||
rows.append([math.nan, math.nan, math.nan, math.nan])
|
||||
else:
|
||||
rows.append(list(v))
|
||||
streamed = np.array(rows, dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_atr_streaming_matches_batch(ohlc_series):
|
||||
high, low, close = ohlc_series
|
||||
batch = ta.ATR(14).batch(high, low, close)
|
||||
|
||||
streamer = ta.ATR(14)
|
||||
rows = []
|
||||
for h, l, c in zip(high, low, close):
|
||||
rows.append(streamer.update((float(c), float(h), float(l), float(c), 0.0, 0)))
|
||||
streamed = np.array([math.nan if v is None else v for v in rows], dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_stochastic_streaming_matches_batch(ohlc_series):
|
||||
high, low, close = ohlc_series
|
||||
batch = ta.Stochastic(14, 3).batch(high, low, close)
|
||||
|
||||
streamer = ta.Stochastic(14, 3)
|
||||
rows = []
|
||||
for h, l, c in zip(high, low, close):
|
||||
v = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
|
||||
rows.append([math.nan, math.nan] if v is None else list(v))
|
||||
streamed = np.array(rows, dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_obv_streaming_matches_batch(ohlc_series):
|
||||
_, _, close = ohlc_series
|
||||
volume = np.ones_like(close)
|
||||
batch = ta.OBV().batch(close, volume)
|
||||
|
||||
streamer = ta.OBV()
|
||||
rows = []
|
||||
for c, v in zip(close, volume):
|
||||
rows.append(streamer.update((float(c), float(c), float(c), float(c), float(v), 0)))
|
||||
streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
Reference in New Issue
Block a user