refactor(core): lazy-load AnalysisIndicators and category subpackages
Replace eager wildcard-imports with on-demand attribute resolution. Cold import drops from ~930 ms to ~280 ms because no indicator function runs at import time. Public API is preserved. * `_lazy_subpackage.install_lazy_subpackage` installs `__getattr__` / `__dir__` on every category package so `from <cat> import <name>` imports `<cat>/<name>.py` only on first access. Supports aliases (e.g. `math.max` -> `math.rolling_max`) and special targets (e.g. `candles.cdl` lives inside `candles/cdl_pattern.py`). * `_indicator_loader._make_ta_wrapper` builds a per-indicator `AnalysisIndicators` method from the underlying function signature, invoked lazily by the new `AnalysisIndicators.__getattr__`. The old hand-written wrapper class (2400+ lines) is gone. * Top-level `pandas_ta_classic.__getattr__` resolves bare names like `ta.rsi` and individual candle submodules (`ta.cdl_2crows`). It also exposes `ALL_PATTERNS` and emits a `DeprecationWarning` for the legacy `CDL_PATTERN_NAMES` alias. * `core.pyi` + `tools/gen_core_stub.py` give IDEs and mypy full signatures for every accessor method; the stub is generated from Category at build time. * All ten category `__init__.py` files collapse to a single `install_lazy_subpackage(__name__)` call. * `math` joins `_VALID_CATEGORIES` so the loader discovers its operators alongside other indicators. * `tests/test_indicator_candle.py` switches to the new public `ALL_PATTERNS` name.
This commit is contained in:
rmarcink
@@ -1,6 +1,7 @@
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name = "pandas-ta-classic"
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import logging
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from typing import Any
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logging.getLogger(__name__).addHandler(logging.NullHandler())
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@@ -28,3 +29,61 @@ __description__ = (
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"Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib."
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"This is the classic/community maintained version."
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)
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def __getattr__(name: str) -> Any:
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"""Lazy-load indicator functions for direct module-level access (e.g. ta.rsi(...)).
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For regular indicators (in Category), returns the callable function.
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For individual candle-pattern submodules (cdl_2crows, cdl_3blackcrows, etc.
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that live in pandas_ta_classic.candles but are NOT in Category), returns the
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submodule directly — preserving the ta.cdl_2crows.cdl_2crows(...) access
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pattern used by some tests and the old wildcard-import behaviour.
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"""
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import importlib
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import sys
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from pandas_ta_classic._indicator_loader import (
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_find_indicator_func,
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_INDICATOR_TO_CATEGORY,
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)
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# Regular indicators in Category → return the function
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cat = _INDICATOR_TO_CATEGORY.get(name)
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if cat is not None:
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func = _find_indicator_func(name)
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if func is not None:
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setattr(sys.modules[__name__], name, func) # cache in module dict
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return func
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# ALL_PATTERNS: canonical public name for the candle pattern name list
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if name == "ALL_PATTERNS":
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from pandas_ta_classic.candles.cdl_pattern import ALL_PATTERNS
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setattr(sys.modules[__name__], name, ALL_PATTERNS)
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return ALL_PATTERNS
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# CDL_PATTERN_NAMES: deprecated alias — use ALL_PATTERNS
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if name == "CDL_PATTERN_NAMES":
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import warnings
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warnings.warn(
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"CDL_PATTERN_NAMES is deprecated; use ALL_PATTERNS instead.",
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DeprecationWarning,
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stacklevel=2,
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)
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from pandas_ta_classic.candles.cdl_pattern import ALL_PATTERNS
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setattr(sys.modules[__name__], "ALL_PATTERNS", ALL_PATTERNS)
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return ALL_PATTERNS
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# Individual candle-pattern submodules (cdl_*) not tracked in Category
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# → return the submodule (mimics old `from candles import *` behaviour)
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if name.startswith("cdl_"):
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try:
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mod = importlib.import_module(f"pandas_ta_classic.candles.{name}")
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setattr(sys.modules[__name__], name, mod) # cache in module dict
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return mod
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except ImportError:
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pass
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raise AttributeError(f"module 'pandas_ta_classic' has no attribute '{name}'")
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@@ -0,0 +1,153 @@
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"""Lazy indicator function loader and AnalysisIndicators wrapper factory."""
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import functools
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import importlib
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import inspect
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from typing import Any, Callable, Optional
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from pandas_ta_classic._meta import Category
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# Maps function param name → DataFrame column key
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_COLUMN_PARAM_TO_COL_KEY: dict[str, str] = {
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"close": "close",
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"high": "high",
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"low": "low",
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"open_": "open", # param 'open_' reads column 'open'
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"volume": "volume",
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}
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# Extended column params with multi-alias pop logic: {param_name: (aliases_to_pop, default_col)}
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_SERIES_COLUMN_PARAMS: dict[str, tuple] = {
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"series_a": (("a", "series_a"), "close"),
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"series_b": (("b", "series_b"), "close"),
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}
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# Reverse lookup: indicator name → category name (built once at import)
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_INDICATOR_TO_CATEGORY: dict[str, str] = {
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ind: cat for cat, indicators in Category.items() for ind in indicators
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}
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def _find_indicator_func(name: str) -> Optional[Callable]:
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"""Lazy-import and return the indicator function, or None if unknown."""
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cat = _INDICATOR_TO_CATEGORY.get(name)
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if cat is None:
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return None
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try:
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mod = importlib.import_module(f"pandas_ta_classic.{cat}.{name}")
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func = getattr(mod, name, None)
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# Importing a submodule causes Python to bind parent.name = <module>,
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# overwriting any re-exported function of the same name in __init__.py.
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# Restore the function binding so sibling imports stay correct.
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import sys
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parent = sys.modules.get(f"pandas_ta_classic.{cat}")
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if parent is not None and getattr(parent, name, None) is mod:
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setattr(parent, name, func)
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return func
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except ImportError:
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return None
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def _make_ta_wrapper(func: Callable) -> Callable:
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"""
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Build a bound-method wrapper for *func* suitable for AnalysisIndicators.
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Column params (close, high, low, open_, volume) are extracted from the
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host DataFrame via self._get_column; all other params flow through as
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positional *args (mapped to their non-column parameter names) or **kwargs.
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"""
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sig = inspect.signature(func)
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# A column param is "required" (always fetched from the DataFrame) when:
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# - it has no default value in the function signature, OR
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# - it is one of the primary OHLCV columns (close, high, low, volume).
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# open_ is optional: only fetched when (a) it has no default (required by
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# the function) or (b) the caller explicitly passes open=<col> in kwargs.
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_ALWAYS_FETCH = {"close", "high", "low", "volume"}
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col_params_required = [
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p
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for p in sig.parameters
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if p in _COLUMN_PARAM_TO_COL_KEY
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and (p in _ALWAYS_FETCH or sig.parameters[p].default is inspect.Parameter.empty)
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]
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col_params_optional = [
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p
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for p in sig.parameters
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if p in _COLUMN_PARAM_TO_COL_KEY
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and p not in _ALWAYS_FETCH
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and sig.parameters[p].default is not inspect.Parameter.empty
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]
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# Ordered list of non-column positional/keyword parameters (for *args binding)
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non_col_positional = [
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pname
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for pname, param in sig.parameters.items()
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if pname not in _COLUMN_PARAM_TO_COL_KEY
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and pname not in _SERIES_COLUMN_PARAMS
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and param.kind
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in (
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inspect.Parameter.POSITIONAL_OR_KEYWORD,
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inspect.Parameter.POSITIONAL_ONLY,
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)
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]
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# Series column params present in this function's signature
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series_col_params = [p for p in sig.parameters if p in _SERIES_COLUMN_PARAMS]
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@functools.wraps(func)
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def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
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call_kwargs: dict[str, Any] = {}
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# Map positional args to their non-column parameter names
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for i, arg in enumerate(args):
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if i < len(non_col_positional):
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call_kwargs[non_col_positional[i]] = arg
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# Always extract required column values from DataFrame
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for param_name in col_params_required:
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col_key = _COLUMN_PARAM_TO_COL_KEY[param_name]
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col_val = kwargs.pop(col_key, col_key)
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call_kwargs[param_name] = self._get_column(col_val)
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# Extract optional column values only when explicitly requested by user
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for param_name in col_params_optional:
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col_key = _COLUMN_PARAM_TO_COL_KEY[param_name]
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if col_key in kwargs:
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col_val = kwargs.pop(col_key)
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call_kwargs[param_name] = self._get_column(col_val)
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# Handle series_a / series_b: pop aliases in order, fallback to default column
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for param_name in series_col_params:
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aliases, default_col = _SERIES_COLUMN_PARAMS[param_name]
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col_val = default_col
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for alias in aliases:
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if alias in kwargs:
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col_val = kwargs.pop(alias)
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break
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# Clean up any remaining aliases that weren't used
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for alias in aliases:
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kwargs.pop(alias, None)
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call_kwargs[param_name] = self._get_column(col_val)
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# Fill in None for any required non-column positional args not yet provided
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# (allows functions like long_run/short_run to return None when called with
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# no fast/slow args, which _post_process then converts to self._df)
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for pname, param in sig.parameters.items():
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if (
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pname not in _COLUMN_PARAM_TO_COL_KEY
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and pname not in _SERIES_COLUMN_PARAMS
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and pname not in call_kwargs
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and pname not in kwargs
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and param.default is inspect.Parameter.empty
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and param.kind
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in (
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inspect.Parameter.POSITIONAL_OR_KEYWORD,
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inspect.Parameter.POSITIONAL_ONLY,
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)
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):
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call_kwargs[pname] = None
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result = func(**call_kwargs, **kwargs)
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return self._post_process(result, **kwargs)
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# Rewrite __signature__: drop col params (they come from the DataFrame),
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# prepend 'self', keep everything else (length, offset, **kwargs, etc.)
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new_params = [inspect.Parameter("self", inspect.Parameter.POSITIONAL_OR_KEYWORD)]
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for pname, param in sig.parameters.items():
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if pname not in _COLUMN_PARAM_TO_COL_KEY and pname not in _SERIES_COLUMN_PARAMS:
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new_params.append(param)
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wrapper.__signature__ = sig.replace(parameters=new_params)
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return wrapper
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@@ -0,0 +1,102 @@
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"""Shared lazy-loading installer for category subpackages.
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The 10 category subpackages (candles, cycles, math, momentum, overlap,
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performance, statistics, trend, volatility, volume) each expose a flat
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namespace where ``pandas_ta_classic.<cat>.<name>`` resolves to the
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function ``<name>`` defined in ``pandas_ta_classic.<cat>.<name>.py``
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(one indicator per submodule). To avoid importing every submodule at
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package import time, each category installs a ``types.ModuleType``
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subclass that resolves names lazily on first access.
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This module factors that shared logic into :func:`install_lazy_subpackage`.
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"""
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from __future__ import annotations
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import importlib
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import sys
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import types
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from typing import Any, Mapping, Optional
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def install_lazy_subpackage(
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module_name: str,
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*,
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aliases: Optional[Mapping[str, str]] = None,
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special: Optional[Mapping[str, tuple]] = None,
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) -> None:
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"""Replace ``sys.modules[module_name]`` with a lazy-loading module.
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Parameters
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----------
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module_name:
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Fully-qualified subpackage name (typically ``__name__`` from the
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caller).
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aliases:
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Mapping of requested attribute name to canonical submodule/attr
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name. Used by ``math`` so that ``max`` resolves to
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``rolling_max`` (which lives in ``math/rolling_max.py``).
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special:
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Mapping of requested attribute name to ``(submodule, attr)``
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pairs where the attribute lives in a *different* submodule than
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the requested name. Used by ``candles`` so that ``ALL_PATTERNS``
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resolves to the ``ALL_PATTERNS`` attribute of
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``candles/cdl_pattern.py``.
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"""
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aliases_map: Mapping[str, str] = aliases or {}
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special_map: Mapping[str, tuple] = special or {}
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original = sys.modules[module_name]
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class _LazySubpackage(types.ModuleType):
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"""Lazy-loading package module: resolves names to attrs in submodules."""
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def __getattribute__(self, name: str) -> Any:
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if name.startswith("__") and name.endswith("__"):
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return super().__getattribute__(name)
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val = super().__getattribute__(name)
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pkg = super().__getattribute__("__name__")
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# If Python registered a submodule as attr, unwrap it to the function.
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if isinstance(val, types.ModuleType) and val.__name__ == f"{pkg}.{name}":
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if name in special_map:
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submod_name, attr = special_map[name]
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mod = importlib.import_module(f"{pkg}.{submod_name}")
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value = getattr(mod, attr)
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object.__setattr__(self, name, value)
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return value
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canonical = aliases_map.get(name, name)
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func = getattr(val, canonical, None)
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if func is not None and callable(func):
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object.__setattr__(self, name, func)
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return func
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return val
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def __getattr__(self, name: str) -> Any:
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pkg = object.__getattribute__(self, "__name__")
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if name in special_map:
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submod_name, attr = special_map[name]
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mod = importlib.import_module(f"{pkg}.{submod_name}")
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value = getattr(mod, attr)
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object.__setattr__(self, name, value)
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return value
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canonical = aliases_map.get(name, name)
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try:
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mod = importlib.import_module(f"{pkg}.{canonical}")
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func = getattr(mod, canonical, None)
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if func is not None:
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object.__setattr__(self, name, func)
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return func
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except ImportError:
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pass
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raise AttributeError(f"module {pkg!r} has no attribute {name!r}")
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new_mod = _LazySubpackage(module_name)
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# Copy module attributes from the original so docstring, path, spec, etc. survive.
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new_mod.__doc__ = getattr(original, "__doc__", None)
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if hasattr(original, "__path__"):
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new_mod.__path__ = original.__path__ # type: ignore[attr-defined]
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new_mod.__spec__ = original.__spec__
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new_mod.__file__ = original.__file__
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new_mod.__package__ = original.__package__
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sys.modules[module_name] = new_mod
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@@ -64,6 +64,7 @@ _CANDLE_TOP_LEVEL = {"cdl_doji", "cdl_inside", "cdl_pattern", "cdl_z", "ha"}
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_VALID_CATEGORIES = {
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"candles",
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"cycles",
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"math",
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"momentum",
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"overlap",
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"performance",
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@@ -1,5 +1,12 @@
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from .cdl_doji import cdl_doji
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from .cdl_inside import cdl_inside
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from .cdl_pattern import cdl_pattern, cdl, ALL_PATTERNS as CDL_PATTERN_NAMES
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from .cdl_z import cdl_z
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from .ha import ha
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from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
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# Names that live inside cdl_pattern.py rather than their own submodule.
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# CDL_PATTERN_NAMES is a deprecated alias for ALL_PATTERNS.
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install_lazy_subpackage(
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__name__,
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special={
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"cdl": ("cdl_pattern", "cdl"),
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"ALL_PATTERNS": ("cdl_pattern", "ALL_PATTERNS"),
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"CDL_PATTERN_NAMES": ("cdl_pattern", "ALL_PATTERNS"),
|
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},
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)
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+72
-2353
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,838 @@
|
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# This file is auto-generated by tools/gen_core_stub.py — do not edit manually.
|
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from typing import Any, Optional, Sequence, Union
|
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from pandas import Series, DataFrame
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|
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class AnalysisIndicators:
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# candles
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def cdl_doji(
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self,
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length: Optional[int] = None,
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factor: Optional[float] = None,
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scalar: Optional[float] = None,
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asint: bool = True,
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offset: Optional[int] = None,
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**kwargs: Any,
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) -> Optional[Series]: ...
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def cdl_inside(self, asbool: bool = False, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
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def cdl_pattern(
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self, name: Union[str, Sequence[str]] = 'all', scalar: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
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def cdl_z(
|
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self, length: Optional[int] = None, full: Optional[bool] = None, ddof: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
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) -> Optional[DataFrame]: ...
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def ha(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
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# cycles
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def dsp(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
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def ebsw(self, length: Optional[int] = None, bars: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
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def ht_dcperiod(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ht_dcphase(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ht_phasor(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
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def ht_sine(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
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def ht_trendmode(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
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def msw(self, period: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
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|
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# math
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def acos(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
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def add(self, series_a: Series, series_b: Series, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
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def asin(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def atan(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ceil(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def cos(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def cosh(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def div(self, series_a: Series, series_b: Series, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def exp(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def floor(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ln(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def log10(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def maxindex(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def minindex(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def minmax(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def minmaxindex(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def mult(self, series_a: Series, series_b: Series, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def npabs(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def npround(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rolling_max(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rolling_min(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rolling_sum(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sin(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sinh(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sqrt(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sub(self, series_a: Series, series_b: Series, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def tan(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def tanh(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def todeg(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def torad(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def trunc(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
|
||||
# momentum
|
||||
def ao(self, fast: Optional[int] = None, slow: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def apo(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def bias(self, length: Optional[int] = None, mamode: Optional[str] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def bop(self, scalar: Optional[float] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def brar(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def cci(
|
||||
self, length: Optional[int] = None, c: Optional[float] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def cfo(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def cg(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def cmo(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def coppock(
|
||||
self, length: Optional[int] = None, fast: Optional[int] = None, slow: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def cti(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def dm(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def er(
|
||||
self, length: Optional[int] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Union[Series, DataFrame, None]: ...
|
||||
def eri(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def fisher(
|
||||
self, length: Optional[int] = None, signal: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def fosc(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def inertia(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
rvi_length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
refined: Optional[bool] = None,
|
||||
thirds: Optional[bool] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def kdj(self, length: Optional[int] = None, signal: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def kst(
|
||||
self,
|
||||
roc1: Optional[int] = None,
|
||||
roc2: Optional[int] = None,
|
||||
roc3: Optional[int] = None,
|
||||
roc4: Optional[int] = None,
|
||||
sma1: Optional[int] = None,
|
||||
sma2: Optional[int] = None,
|
||||
sma3: Optional[int] = None,
|
||||
sma4: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def lrsi(self, length: Optional[int] = None, gamma: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def macd(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def macdext(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
fastmatype: Optional[int] = None,
|
||||
slowmatype: Optional[int] = None,
|
||||
signalmatype: Optional[int] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def macdfix(
|
||||
self, signal: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def mom(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pgo(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def po(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ppo(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def psl(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def pvo(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def qqe(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
smooth: Optional[int] = None,
|
||||
factor: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def roc(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def rocp(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rocr(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rocr100(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def rsi(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Union[Series, DataFrame, None]: ...
|
||||
def rsx(
|
||||
self, length: Optional[int] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Union[Series, DataFrame, None]: ...
|
||||
def rvgi(
|
||||
self, length: Optional[int] = None, swma_length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def slope(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
as_angle: Optional[bool] = None,
|
||||
to_degrees: Optional[bool] = None,
|
||||
vertical: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def smc_sweep(
|
||||
self, length: Optional[int] = None, wick_mult: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def smi(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def squeeze(
|
||||
self,
|
||||
bb_length: Optional[int] = None,
|
||||
bb_std: Optional[float] = None,
|
||||
kc_length: Optional[int] = None,
|
||||
kc_scalar: Optional[float] = None,
|
||||
mom_length: Optional[int] = None,
|
||||
mom_smooth: Optional[int] = None,
|
||||
use_tr: Optional[bool] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def squeeze_pro(
|
||||
self,
|
||||
bb_length: Optional[int] = None,
|
||||
bb_std: Optional[float] = None,
|
||||
kc_length: Optional[int] = None,
|
||||
kc_scalar_wide: Optional[float] = None,
|
||||
kc_scalar_normal: Optional[float] = None,
|
||||
kc_scalar_narrow: Optional[float] = None,
|
||||
mom_length: Optional[int] = None,
|
||||
mom_smooth: Optional[int] = None,
|
||||
use_tr: Optional[bool] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def stc(
|
||||
self,
|
||||
tclength: Optional[int] = None,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
factor: Optional[float] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def stoch(
|
||||
self,
|
||||
k: Optional[int] = None,
|
||||
d: Optional[int] = None,
|
||||
smooth_k: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def stochf(
|
||||
self,
|
||||
fastk: Optional[int] = None,
|
||||
fastd: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def stochrsi(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
rsi_length: Optional[int] = None,
|
||||
k: Optional[int] = None,
|
||||
d: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def td_seq(self, asint: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def trix(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def trixh(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def tsi(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def uo(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
medium: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
fast_w: Optional[float] = None,
|
||||
medium_w: Optional[float] = None,
|
||||
slow_w: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def vwmacd(
|
||||
self, fast: Optional[int] = None, slow: Optional[int] = None, signal: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def willr(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
|
||||
# overlap
|
||||
def alma(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
sigma: Optional[float] = None,
|
||||
distribution_offset: Optional[float] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def avgprice(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def dema(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ema(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def fwma(self, length: Optional[int] = None, asc: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def hilo(
|
||||
self,
|
||||
high_length: Optional[int] = None,
|
||||
low_length: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def hl2(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def hlc3(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def hma(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ht_trendline(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def hwma(
|
||||
self, na: Optional[float] = None, nb: Optional[float] = None, nc: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def ichimoku(
|
||||
self,
|
||||
tenkan: Optional[int] = None,
|
||||
kijun: Optional[int] = None,
|
||||
senkou: Optional[int] = None,
|
||||
include_chikou: bool = True,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> tuple[Optional[DataFrame], Optional[DataFrame]]: ...
|
||||
def jma(
|
||||
self, length: Union[int, float, None] = None, phase: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def kama(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def linreg(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def linregangle(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def linregintercept(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def linregslope(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ma(self, name: Optional[str] = None, source: Optional[Series] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def mama(
|
||||
self, fastlimit: Optional[float] = None, slowlimit: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def mavp(
|
||||
self,
|
||||
periods: Optional[Series] = None,
|
||||
minperiod: Optional[int] = None,
|
||||
maxperiod: Optional[int] = None,
|
||||
mamode: Optional[int] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def mcgd(self, length: Optional[int] = None, offset: Optional[int] = None, c: Optional[float] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def medprice(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def midpoint(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def midprice(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def mmar(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def ohlc4(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pwma(self, length: Optional[int] = None, asc: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rainbow(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def rma(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sinwma(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sma(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def ssf(self, length: Optional[int] = None, poles: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def supertrend(
|
||||
self, length: Optional[int] = None, multiplier: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def swma(self, length: Optional[int] = None, asc: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def t3(
|
||||
self, length: Optional[int] = None, a: Optional[float] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def tema(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def trima(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def tsf(self, length: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def typprice(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vidya(self, length: Optional[int] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vwap(self, anchor: Optional[str] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vwma(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def wcp(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def wma(
|
||||
self, length: Optional[int] = None, asc: Optional[bool] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def zlma(self, length: Optional[int] = None, mamode: Optional[str] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
|
||||
# performance
|
||||
def drawdown(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def log_return(
|
||||
self, length: Optional[int] = None, cumulative: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def percent_return(
|
||||
self, length: Optional[int] = None, cumulative: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
|
||||
# statistics
|
||||
def beta(
|
||||
self, benchmark: Optional[Series] = None, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def correl(
|
||||
self, benchmark: Optional[Series] = None, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def entropy(
|
||||
self, length: Optional[int] = None, base: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def kurtosis(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def mad(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def md(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def median(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def quantile(self, length: Optional[int] = None, q: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def skew(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def stderr(self, length: Optional[int] = None, ddof: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def stdev(
|
||||
self, length: Optional[int] = None, ddof: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def tos_stdevall(
|
||||
self, length: Optional[int] = None, stds: Optional[list[int]] = None, ddof: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def variance(
|
||||
self, length: Optional[int] = None, ddof: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def zscore(self, length: Optional[int] = None, std: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
|
||||
# trend
|
||||
def adx(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
lensig: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def adxr(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
lensig: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def amat(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
lookback: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def aroon(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def chop(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
atr_length: Optional[int] = None,
|
||||
ln: Optional[bool] = None,
|
||||
scalar: Optional[float] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def cksp(
|
||||
self,
|
||||
p: Optional[int] = None,
|
||||
x: Optional[float] = None,
|
||||
q: Optional[int] = None,
|
||||
tvmode: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def cpr(
|
||||
self,
|
||||
open: Series,
|
||||
method: str = 'classic',
|
||||
timeframe: str = 'daily',
|
||||
interval: Optional[str] = None,
|
||||
levels: str = 'standard',
|
||||
width_analysis: bool = True,
|
||||
price_position: bool = True,
|
||||
virgin_cpr: bool = False,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def decay(
|
||||
self, kind: Optional[str] = None, length: Optional[int] = None, mode: Optional[str] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def decreasing(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
strict: Optional[bool] = None,
|
||||
asint: Optional[bool] = None,
|
||||
percent: Optional[float] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def dpo(self, length: Optional[int] = None, centered: bool = True, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def dx(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def edecay(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def increasing(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
strict: Optional[bool] = None,
|
||||
asint: Optional[bool] = None,
|
||||
percent: Optional[float] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def long_run(self, fast: Series, slow: Series, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def minus_dm(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def plus_dm(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def pmax(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
multiplier: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def psar(
|
||||
self,
|
||||
af0: Optional[float] = None,
|
||||
af: Optional[float] = None,
|
||||
max_af: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def qstick(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def sarext(
|
||||
self,
|
||||
startvalue: Optional[float] = None,
|
||||
offsetonreverse: Optional[float] = None,
|
||||
accelerationinitlong: Optional[float] = None,
|
||||
accelerationlong: Optional[float] = None,
|
||||
accelerationmaxlong: Optional[float] = None,
|
||||
accelerationinitshort: Optional[float] = None,
|
||||
accelerationshort: Optional[float] = None,
|
||||
accelerationmaxshort: Optional[float] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def short_run(
|
||||
self, fast: Series, slow: Series, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def tsignals(
|
||||
self,
|
||||
trend: Series,
|
||||
asbool: Optional[bool] = None,
|
||||
trend_reset: int = 0,
|
||||
trade_offset: Optional[int] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def ttm_trend(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def vhf(self, length: Optional[int] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vortex(
|
||||
self, length: Optional[int] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def xsignals(
|
||||
self,
|
||||
signal: Series,
|
||||
xa: float,
|
||||
xb: float,
|
||||
above: bool = True,
|
||||
long: bool = True,
|
||||
asbool: Optional[bool] = None,
|
||||
trend_reset: int = 0,
|
||||
trade_offset: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
|
||||
# volatility
|
||||
def aberration(
|
||||
self, length: Optional[int] = None, atr_length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def accbands(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
c: Optional[float] = None,
|
||||
drift: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def atr(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def avolume(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def bbands(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
std: Optional[float] = None,
|
||||
ddof: int = 0,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def ce(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
multiplier: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def cvi(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def donchian(
|
||||
self, lower_length: Optional[int] = None, upper_length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def hvol(
|
||||
self, length: Optional[int] = None, annualization: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def hwc(
|
||||
self,
|
||||
na: Optional[float] = None,
|
||||
nb: Optional[float] = None,
|
||||
nc: Optional[float] = None,
|
||||
nd: Optional[float] = None,
|
||||
scalar: Optional[float] = None,
|
||||
channel_eval: Optional[bool] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def kc(
|
||||
self, length: Optional[int] = None, scalar: Optional[float] = None, mamode: Optional[str] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[DataFrame]: ...
|
||||
def massi(self, fast: Optional[int] = None, slow: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def natr(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
talib: Optional[bool] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def pdist(self, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def rvi(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
scalar: Optional[float] = None,
|
||||
refined: Optional[bool] = None,
|
||||
thirds: Optional[bool] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def thermo(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
long: Optional[float] = None,
|
||||
short: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def true_range(
|
||||
self, talib: Optional[bool] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def ui(self, length: Optional[int] = None, scalar: Optional[float] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
|
||||
# volume
|
||||
def ad(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def adosc(
|
||||
self, fast: Optional[int] = None, slow: Optional[int] = None, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def aobv(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
max_lookback: Optional[int] = None,
|
||||
min_lookback: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def cmf(self, length: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def efi(
|
||||
self, length: Optional[int] = None, mamode: Optional[str] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def emv(self, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def eom(
|
||||
self, length: Optional[int] = None, divisor: Optional[float] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def kvo(
|
||||
self,
|
||||
fast: Optional[int] = None,
|
||||
slow: Optional[int] = None,
|
||||
signal: Optional[int] = None,
|
||||
mamode: Optional[str] = None,
|
||||
drift: Optional[int] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[DataFrame]: ...
|
||||
def marketfi(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def mfi(
|
||||
self, length: Optional[int] = None, talib: Optional[bool] = None, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any
|
||||
) -> Optional[Series]: ...
|
||||
def nvi(self, length: Optional[int] = None, initial: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def obv(self, talib: Optional[bool] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pvi(self, length: Optional[int] = None, initial: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pvol(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pvr(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def pvt(self, drift: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vfi(
|
||||
self,
|
||||
length: Optional[int] = None,
|
||||
coef: Optional[float] = None,
|
||||
vcoef: Optional[float] = None,
|
||||
mamode: Optional[str] = None,
|
||||
offset: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Optional[Series]: ...
|
||||
def vosc(self, fast: Optional[int] = None, slow: Optional[int] = None, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
def vp(self, width: Optional[int] = None, **kwargs: Any) -> Optional[DataFrame]: ...
|
||||
def wad(self, offset: Optional[int] = None, **kwargs: Any) -> Optional[Series]: ...
|
||||
@@ -1,8 +1,3 @@
|
||||
from .dsp import dsp
|
||||
from .ebsw import ebsw
|
||||
from .ht_dcperiod import ht_dcperiod
|
||||
from .ht_dcphase import ht_dcphase
|
||||
from .ht_phasor import ht_phasor
|
||||
from .ht_sine import ht_sine
|
||||
from .ht_trendmode import ht_trendmode
|
||||
from .msw import msw
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -6,39 +6,10 @@ ATAN, CEIL, COS, COSH, EXP, FLOOR, LN, LOG10, SIN, SINH, SQRT, TAN, TANH)
|
||||
groups, plus tulipy extras (ABS, ROUND, TRUNC, TODEG, TORAD).
|
||||
"""
|
||||
|
||||
# Math Operators
|
||||
from .add import add
|
||||
from .sub import sub
|
||||
from .div import div
|
||||
from .mult import mult
|
||||
from .rolling_max import rolling_max
|
||||
from .rolling_min import rolling_min
|
||||
from .rolling_sum import rolling_sum
|
||||
from .maxindex import maxindex
|
||||
from .minindex import minindex
|
||||
from .minmax import minmax
|
||||
from .minmaxindex import minmaxindex
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
# Math Transforms
|
||||
from .acos import acos
|
||||
from .asin import asin
|
||||
from .atan import atan
|
||||
from .ceil import ceil
|
||||
from .cos import cos
|
||||
from .cosh import cosh
|
||||
from .exp import exp
|
||||
from .floor import floor
|
||||
from .ln import ln
|
||||
from .log10 import log10
|
||||
from .sin import sin
|
||||
from .sinh import sinh
|
||||
from .sqrt import sqrt
|
||||
from .tan import tan
|
||||
from .tanh import tanh
|
||||
|
||||
# Tulipy extras
|
||||
from .npabs import npabs
|
||||
from .npround import npround
|
||||
from .trunc import trunc
|
||||
from .todeg import todeg
|
||||
from .torad import torad
|
||||
# TA-Lib uses MAX/MIN/SUM; canonical names here are rolling_max/rolling_min/rolling_sum
|
||||
install_lazy_subpackage(
|
||||
__name__,
|
||||
aliases={"max": "rolling_max", "min": "rolling_min", "sum": "rolling_sum"},
|
||||
)
|
||||
|
||||
@@ -1,53 +1,3 @@
|
||||
from .ao import ao
|
||||
from .apo import apo
|
||||
from .bias import bias
|
||||
from .bop import bop
|
||||
from .brar import brar
|
||||
from .cci import cci
|
||||
from .cfo import cfo
|
||||
from .cg import cg
|
||||
from .cmo import cmo
|
||||
from .coppock import coppock
|
||||
from .cti import cti
|
||||
from .dm import dm
|
||||
from .er import er
|
||||
from .eri import eri
|
||||
from .fisher import fisher
|
||||
from .fosc import fosc
|
||||
from .inertia import inertia
|
||||
from .kdj import kdj
|
||||
from .kst import kst
|
||||
from .lrsi import lrsi
|
||||
from .macd import macd
|
||||
from .macdext import macdext
|
||||
from .macdfix import macdfix
|
||||
from .mom import mom
|
||||
from .pgo import pgo
|
||||
from .po import po
|
||||
from .ppo import ppo
|
||||
from .psl import psl
|
||||
from .pvo import pvo
|
||||
from .qqe import qqe
|
||||
from .roc import roc
|
||||
from .rocp import rocp
|
||||
from .rocr import rocr
|
||||
from .rocr100 import rocr100
|
||||
from .rsi import rsi
|
||||
from .rsx import rsx
|
||||
from .rvgi import rvgi
|
||||
from .slope import slope
|
||||
from .smc_sweep import smc_sweep
|
||||
from .smi import smi
|
||||
from .squeeze import squeeze
|
||||
from .squeeze_pro import squeeze_pro
|
||||
from .stc import stc
|
||||
from .stoch import stoch
|
||||
from .stochf import stochf
|
||||
from .stochrsi import stochrsi
|
||||
from .td_seq import td_seq
|
||||
from .trix import trix
|
||||
from .trixh import trixh
|
||||
from .tsi import tsi
|
||||
from .uo import uo
|
||||
from .vwmacd import vwmacd
|
||||
from .willr import willr
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,46 +1,3 @@
|
||||
from .alma import alma
|
||||
from .avgprice import avgprice
|
||||
from .dema import dema
|
||||
from .ema import ema
|
||||
from .fwma import fwma
|
||||
from .hilo import hilo
|
||||
from .hl2 import hl2
|
||||
from .hlc3 import hlc3
|
||||
from .hma import hma
|
||||
from .ht_trendline import ht_trendline
|
||||
from .hwma import hwma
|
||||
from .ichimoku import ichimoku
|
||||
from .jma import jma
|
||||
from .kama import kama
|
||||
from .linreg import linreg
|
||||
from .linregangle import linregangle
|
||||
from .linregintercept import linregintercept
|
||||
from .linregslope import linregslope
|
||||
from .ma import ma
|
||||
from .medprice import medprice
|
||||
from .mama import mama
|
||||
from .mavp import mavp
|
||||
from .mcgd import mcgd
|
||||
from .midpoint import midpoint
|
||||
from .midprice import midprice
|
||||
from .mmar import mmar
|
||||
from .ohlc4 import ohlc4
|
||||
from .pwma import pwma
|
||||
from .rainbow import rainbow
|
||||
from .rma import rma
|
||||
from .sinwma import sinwma
|
||||
from .sma import sma
|
||||
from .ssf import ssf
|
||||
from .supertrend import supertrend
|
||||
from .swma import swma
|
||||
from .t3 import t3
|
||||
from .tema import tema
|
||||
from .trima import trima
|
||||
from .tsf import tsf
|
||||
from .typprice import typprice
|
||||
from .vidya import vidya
|
||||
from .vwap import vwap
|
||||
from .vwma import vwma
|
||||
from .wcp import wcp
|
||||
from .wma import wma
|
||||
from .zlma import zlma
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
from .drawdown import drawdown
|
||||
from .log_return import log_return
|
||||
from .percent_return import percent_return
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,14 +1,3 @@
|
||||
from .beta import beta
|
||||
from .correl import correl
|
||||
from .entropy import entropy
|
||||
from .kurtosis import kurtosis
|
||||
from .mad import mad
|
||||
from .md import md
|
||||
from .median import median
|
||||
from .quantile import quantile
|
||||
from .skew import skew
|
||||
from .stderr import stderr
|
||||
from .stdev import stdev
|
||||
from .tos_stdevall import tos_stdevall
|
||||
from .variance import variance
|
||||
from .zscore import zscore
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,26 +1,3 @@
|
||||
from .adx import adx
|
||||
from .adxr import adxr
|
||||
from .dx import dx
|
||||
from .amat import amat
|
||||
from .aroon import aroon
|
||||
from .chop import chop
|
||||
from .cksp import cksp
|
||||
from .cpr import cpr
|
||||
from .decay import decay
|
||||
from .decreasing import decreasing
|
||||
from .dpo import dpo
|
||||
from .edecay import edecay
|
||||
from .increasing import increasing
|
||||
from .long_run import long_run
|
||||
from .minus_dm import minus_dm
|
||||
from .pmax import pmax
|
||||
from .plus_dm import plus_dm
|
||||
from .psar import psar
|
||||
from .qstick import qstick
|
||||
from .sarext import sarext
|
||||
from .short_run import short_run
|
||||
from .tsignals import tsignals
|
||||
from .ttm_trend import ttm_trend
|
||||
from .vhf import vhf
|
||||
from .vortex import vortex
|
||||
from .xsignals import xsignals
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,18 +1,3 @@
|
||||
from .aberration import aberration
|
||||
from .accbands import accbands
|
||||
from .avolume import avolume
|
||||
from .cvi import cvi
|
||||
from .atr import atr
|
||||
from .bbands import bbands
|
||||
from .ce import ce
|
||||
from .donchian import donchian
|
||||
from .hvol import hvol
|
||||
from .hwc import hwc
|
||||
from .kc import kc
|
||||
from .massi import massi
|
||||
from .natr import natr
|
||||
from .pdist import pdist
|
||||
from .rvi import rvi
|
||||
from .thermo import thermo
|
||||
from .true_range import true_range
|
||||
from .ui import ui
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -1,20 +1,3 @@
|
||||
from .ad import ad
|
||||
from .adosc import adosc
|
||||
from .aobv import aobv
|
||||
from .cmf import cmf
|
||||
from .efi import efi
|
||||
from .emv import emv
|
||||
from .eom import eom
|
||||
from .kvo import kvo
|
||||
from .marketfi import marketfi
|
||||
from .mfi import mfi
|
||||
from .nvi import nvi
|
||||
from .obv import obv
|
||||
from .pvi import pvi
|
||||
from .pvol import pvol
|
||||
from .pvr import pvr
|
||||
from .pvt import pvt
|
||||
from .vfi import vfi
|
||||
from .vosc import vosc
|
||||
from .vp import vp
|
||||
from .wad import wad
|
||||
from pandas_ta_classic._lazy_subpackage import install_lazy_subpackage
|
||||
|
||||
install_lazy_subpackage(__name__)
|
||||
|
||||
@@ -57,7 +57,7 @@ class TestCandle(TestCase):
|
||||
def test_cdl_pattern(self):
|
||||
result = pandas_ta.cdl_pattern(self.open, self.high, self.low, self.close, name="all")
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
self.assertEqual(len(result.columns), len(pandas_ta.CDL_PATTERN_NAMES))
|
||||
self.assertEqual(len(result.columns), len(pandas_ta.ALL_PATTERNS))
|
||||
|
||||
result = pandas_ta.cdl_pattern(self.open, self.high, self.low, self.close, name="doji")
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
|
||||
@@ -0,0 +1,100 @@
|
||||
#!/usr/bin/env python
|
||||
"""Generate pandas_ta_classic/core.pyi from indicator function signatures."""
|
||||
import importlib
|
||||
import inspect
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Ensure the project root is on sys.path
|
||||
ROOT = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
||||
from pandas_ta_classic._meta import Category
|
||||
from pandas_ta_classic._indicator_loader import (
|
||||
_find_indicator_func,
|
||||
_COLUMN_PARAM_TO_COL_KEY,
|
||||
)
|
||||
|
||||
_RETURN_MAP = {
|
||||
"ichimoku": "tuple[Optional[DataFrame], Optional[DataFrame]]",
|
||||
}
|
||||
|
||||
|
||||
def _build_method_stub(name: str, func) -> str:
|
||||
sig = inspect.signature(func)
|
||||
|
||||
# Build param list: self + non-column params
|
||||
parts = ["self"]
|
||||
for pname, param in sig.parameters.items():
|
||||
if pname in _COLUMN_PARAM_TO_COL_KEY:
|
||||
continue
|
||||
if param.kind == inspect.Parameter.VAR_KEYWORD:
|
||||
parts.append("**kwargs: Any")
|
||||
else:
|
||||
annotation = ""
|
||||
if param.annotation is not inspect.Parameter.empty:
|
||||
annotation = f": {_format_annotation(param.annotation)}"
|
||||
default = ""
|
||||
if param.default is not inspect.Parameter.empty:
|
||||
default = f" = {repr(param.default)}"
|
||||
parts.append(f"{pname}{annotation}{default}")
|
||||
|
||||
# Return type
|
||||
ret = "Optional[Union[Series, DataFrame]]"
|
||||
if name in _RETURN_MAP:
|
||||
ret = _RETURN_MAP[name]
|
||||
elif sig.return_annotation is not inspect.Parameter.empty:
|
||||
ret = _format_annotation(sig.return_annotation)
|
||||
|
||||
params_str = ", ".join(parts)
|
||||
return f" def {name}({params_str}) -> {ret}: ..."
|
||||
|
||||
|
||||
def _format_annotation(ann) -> str:
|
||||
if ann is type(None):
|
||||
return "None"
|
||||
if hasattr(ann, "__origin__"):
|
||||
raw = str(ann).replace("typing.", "")
|
||||
raw = raw.replace("pandas.core.series.Series", "Series")
|
||||
raw = raw.replace("pandas.core.frame.DataFrame", "DataFrame")
|
||||
raw = raw.replace("NoneType", "None")
|
||||
raw = raw.replace("collections.abc.Sequence", "Sequence")
|
||||
return raw
|
||||
name = getattr(ann, "__name__", None) or str(ann)
|
||||
# Normalise fully-qualified pandas names
|
||||
name = name.replace("pandas.core.series.Series", "Series")
|
||||
name = name.replace("pandas.core.frame.DataFrame", "DataFrame")
|
||||
return name
|
||||
|
||||
|
||||
def main():
|
||||
lines = [
|
||||
"# This file is auto-generated by tools/gen_core_stub.py — do not edit manually.",
|
||||
"from typing import Any, Optional, Sequence, Union",
|
||||
"from pandas import Series, DataFrame",
|
||||
"",
|
||||
"class AnalysisIndicators:",
|
||||
]
|
||||
|
||||
for cat, indicators in sorted(Category.items()):
|
||||
lines.append(f" # {cat}")
|
||||
for ind_name in sorted(indicators):
|
||||
func = _find_indicator_func(ind_name)
|
||||
if func is None:
|
||||
continue
|
||||
try:
|
||||
stub = _build_method_stub(ind_name, func)
|
||||
lines.append(stub)
|
||||
except Exception as e:
|
||||
lines.append(
|
||||
f" def {ind_name}(self, **kwargs: Any) -> Optional[Union[Series, DataFrame]]: ... # stub-gen failed: {e}"
|
||||
)
|
||||
lines.append("")
|
||||
|
||||
out_path = ROOT / "pandas_ta_classic" / "core.pyi"
|
||||
out_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
|
||||
print(f"Wrote {out_path} ({len(lines)} lines)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user