done ?

pandas_ta/__init__.py

@@ -6,6 +6,7 @@ name = "pandas_ta"
 # Dictionaries and version
 from pandas_ta.maps import EXCHANGE_TZ, RATE, Category, Imports, version
 from pandas_ta.utils import *
+from pandas_ta.utils import __all__ as utils_all
 
 # Flat Structure. Supports ta.ema() or ta.overlap.ema() calls.
 from pandas_ta.candles import *
@@ -18,6 +19,16 @@ from pandas_ta.transform import *
 from pandas_ta.trend import *
 from pandas_ta.volatility import *
 from pandas_ta.volume import *
+from pandas_ta.candles import __all__ as candles_all
+from pandas_ta.cycles import __all__ as cycles_all
+from pandas_ta.momentum import __all__ as momentum_all
+from pandas_ta.overlap import __all__ as overlap_all
+from pandas_ta.performance import __all__ as performance_all
+from pandas_ta.statistics import __all__ as statistics_all
+from pandas_ta.transform import __all__ as transform_all
+from pandas_ta.trend import __all__ as trend_all
+from pandas_ta.volatility import __all__ as volatility_all
+from pandas_ta.volume import __all__ as volume_all
 
 # Common Averages useful for Indicators with a mamode argument, like ta.adx()
 from pandas_ta.ma import ma
@@ -28,5 +39,29 @@ from pandas_ta.custom import create_dir, import_dir
 # Enable "ta" DataFrame Extension
 from pandas_ta.core import AnalysisIndicators
 
-# Empty DataFrame Alias. Example: df = ta.df  vs.  df = pd.DataFrame()
-df = DataFrame()
+__all__ = [
+    'name',
+    'EXCHANGE_TZ',
+    'RATE',
+    'Category',
+    'Imports',
+    'version',
+    'ma',
+    'create_dir',
+    'import_dir',
+    'AnalysisIndicators',
+]
+
+__all__ += (
+    utils_all
+    + candles_all
+    + cycles_all
+    + momentum_all
+    + overlap_all
+    + performance_all
+    + statistics_all
+    + transform_all
+    + trend_all
+    + volatility_all
+    + volume_all
+)

pandas_ta/utils/_candles.py

@@ -2,6 +2,12 @@
 from pandas import Series
 from pandas_ta.utils._core import non_zero_range
 
+__all__ = [
+    'candle_color',
+    'high_low_range',
+    'real_body',
+]
+
 
 def candle_color(open_: Series, close: Series) -> Series:
     """Candle Change

pandas_ta/utils/_core.py

@@ -12,6 +12,21 @@ from pandas_ta._typing import Int, IntFloat, ListStr, Union
 from pandas_ta.utils._validate import v_bool, v_pos_default, v_series
 from pandas_ta.maps import Imports
 
+__all__ = [
+    'camelCase2Title',
+    'category_files',
+    'non_zero_range',
+    'recent_maximum_index',
+    'recent_minimum_index',
+    'rma_pandas',
+    'signed_series',
+    'simplify_columns',
+    'tal_ma',
+    'unsigned_differences',
+    'ms2secs',
+    'speed_test',
+]
+
 
 def camelCase2Title(x: str):
     """https://stackoverflow.com/questions/5020906/python-convert-camel-case-to-space-delimited-using-regex-and-taking-acronyms-in"""

pandas_ta/utils/_math.py

@@ -20,6 +20,22 @@ from pandas_ta._typing import (
 from pandas_ta.maps import Imports
 from pandas_ta.utils._validate import v_series
 
+__all__ = [
+    'fibonacci',
+    'erf',
+    'combination',
+    'geometric_mean',
+    'hpoly',
+    'linear_regression',
+    'log_geometric_mean',
+    'pascals_triangle',
+    'strided_window',
+    'symmetric_triangle',
+    'weights',
+    'zero',
+    'df_error_analysis',
+]
+
 
 def combination(
     n: Int = 1, r: Int = 0,
@@ -40,7 +56,6 @@ def combination(
     denominator = reduce(mul, range(1, r + 1), 1)
     return numerator // denominator
 
-
 def erf(x: IntFloat) -> Float:
     """Error Function erf(x)
     The algorithm comes from Handbook of Mathematical Functions, formula 7.1.26.
@@ -63,7 +78,6 @@ def erf(x: IntFloat) -> Float:
                * t + a1) * t * exp(-x * x)
     return x_sign * y  # erf(-x) = -erf(x)
 
-
 def fibonacci(
     n: Int = 2, weighted: bool = False, zero: bool = False
 ) -> Array:
@@ -90,7 +104,6 @@ def fibonacci(
     else:
         return result
 
-
 def geometric_mean(series: Series) -> Float:
     """Returns the Geometric Mean for a Series of positive values."""
     n = series.size

pandas_ta/utils/_metrics.py

@@ -8,6 +8,20 @@ from pandas_ta.utils._validate import v_series
 from pandas_ta.utils._math import linear_regression, log_geometric_mean
 from pandas_ta.utils._time import total_time
 
+__all__ = [
+    'cagr',
+    'calmar_ratio',
+    'downside_deviation',
+    'jensens_alpha',
+    'log_max_drawdown',
+    'max_drawdown'
+    'volatility',
+    'sortino_ratio',
+    'sharpe_ratio',
+    'pure_profit_score',
+    'optimal_leverage',
+]
+
 
 def cagr(close: Series) -> IntFloat:
     """Compounded Annual Growth Rate
@@ -172,7 +186,6 @@ def optimal_leverage(
     amount = int(capital * opt_leverage)
     return amount
 
-
 def pure_profit_score(close: Series) -> IntFloat:
     """Pure Profit Score of a series.
 
@@ -189,7 +202,6 @@ def pure_profit_score(close: Series) -> IntFloat:
         return r * cagr(close)
     return 0
 
-
 def sharpe_ratio(
     close: Series, benchmark_rate: IntFloat = 0.0, log: bool = False,
     use_cagr: bool = False, period: IntFloat = RATE["TRADING_DAYS_PER_YEAR"]
@@ -223,7 +235,6 @@ def sharpe_ratio(
         period_std = sqrt(period) * returns.std()
         return (period_mu - benchmark_rate) / period_std
 
-
 def sortino_ratio(
     close: Series, benchmark_rate: IntFloat = 0.0, log: bool = False
 ) -> IntFloat:
@@ -249,7 +260,6 @@ def sortino_ratio(
     result /= downside_deviation(returns)
     return result
 
-
 def volatility(
     close: Series, tf: str = "years", returns: bool = False, log: bool = False
 ) -> IntFloat:

pandas_ta/utils/_numba.py

@@ -8,6 +8,12 @@ try:
 except ImportError:
     def njit(_): return _
 
+__all__ = [
+    'np_prepend',
+    'np_rolling',
+    'np_shift',
+]
+
 
 # Utilities
 @njit

pandas_ta/utils/_signals.py

@@ -4,6 +4,16 @@ from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils._validate import v_offset, v_series
 from pandas_ta.utils._math import zero
 
+__all__ = [
+    'above',
+    'above_value',
+    'below',
+    'below_value',
+    'cross',
+    'cross_value',
+    'signals',
+]
+
 
 def _above_below(
     series_a: Series, series_b: Series,

pandas_ta/utils/_stats.py

@@ -4,6 +4,10 @@ from pandas_ta._typing import Array, IntFloat, Number, Union
 from pandas_ta.maps import Imports
 from pandas_ta.utils import hpoly
 
+__all__ = [
+    'inv_norm',
+]
+
 
 def _gaussian_poly_coefficients() -> Array:
     """Three pairs of Polynomial Approximation Coefficients

pandas_ta/utils/_study.py

@@ -5,6 +5,15 @@ from dataclasses import dataclass, field
 from pandas_ta._typing import Int, List
 from pandas_ta.utils._time import get_time
 
+__all__ = [
+    'Study',
+    'AllStudy',
+    'CommonStudy',
+    'Strategy',
+    'AllStrategy',
+    'CommonStrategy',
+]
+
 
 # Study DataClass
 @dataclass

pandas_ta/utils/_time.py

@@ -6,6 +6,21 @@ from pandas import DataFrame, Series, Timestamp, to_datetime
 from pandas_ta._typing import Float, MaybeSeriesFrame, Optional, Tuple, Union
 from pandas_ta.maps import EXCHANGE_TZ, RATE
 
+__all__ = [
+    'df_dates',
+    'df_month_to_date',
+    'df_quarter_to_date',
+    'df_year_to_date',
+    'final_time',
+    'get_time',
+    'total_time',
+    'to_utc',
+    'unix_convert'
+    'mtd', 
+    'qtd',
+    'ytd',
+]
+
 
 def df_dates(
     df: DataFrame, dates: Tuple[str, list] = None

pandas_ta/utils/_validate.py

@@ -12,6 +12,28 @@ from pandas_ta._typing import (
     SeriesFrame
 )
 
+__all__ = [
+    'is_percent',
+    'v_bool',
+    'v_dataframe',
+    'v_float',
+    'v_int',
+    'v_str',
+    'v_ascending',
+    'v_datetime_ordered',
+    'v_drift',
+    'v_list',
+    'v_lowerbound',
+    'v_mamode',
+    'v_offset',
+    'v_pos_default',
+    'v_scalar',
+    'v_series',
+    'v_talib',
+    'v_tradingview',
+    'v_upperbound',
+]
+
 
 def is_percent(x: IntFloat) -> bool:
     if isinstance(x, (int, float)):

pandas_ta/volume/__init__.py

@@ -18,3 +18,25 @@ from .vp import vp
 from .vwap import vwap
 from .vwma import vwma
 from .wb_tsv import wb_tsv
+
+__all__ = [
+    'ad',
+    'adosc',
+    'aobv',
+    'cmf',
+    'efi',
+    'eom',
+    'kvo',
+    'mfi',
+    'nvi',
+    'obv',
+    'pvi',
+    'pvo',
+    'pvol',
+    'pvr',
+    'pvt',
+    'vp',
+    'vwap',
+    'vwma',
+    'wb_tsv',
+]