pandas_ta-0.3.02b.tar.gz

# -*- coding: utf-8 -*- from dataclasses import dataclass, field from multiprocessing import cpu_count, Pool from pathlib import Path from time import perf_counter from typing import List, Tuple import pandas as pd from numpy import log10 as npLog10 from numpy import ndarray as npNdarray from pandas.core.base import PandasObject from pandas_ta import Category, Imports, version from pandas_ta.candles.cdl_pattern import ALL_PATTERNS from pandas_ta.candles import * from pandas_ta.cycles import * from pandas_ta.momentum import * from pandas_ta.overlap import * from pandas_ta.performance import * from pandas_ta.statistics import * from pandas_ta.trend import * from pandas_ta.volatility import * from pandas_ta.volume import * from pandas_ta.utils import * df = pd.DataFrame() # Strategy DataClass @dataclass class Strategy: """Strategy DataClass A way to name and group your favorite indicators Args: name (str): Some short memorable string. Note: Case-insensitive "All" is reserved. ta (list of dicts): A list of dicts containing keyword arguments where "kind" is the indicator. description (str): A more detailed description of what the Strategy tries to capture. Default: None created (str): At datetime string of when it was created. Default: Automatically generated. *Subject to change* Example TA: ta = [ {"kind": "sma", "length": 200}, {"kind": "sma", "close": "volume", "length": 50}, {"kind": "bbands", "length": 20}, {"kind": "rsi"}, {"kind": "macd", "fast": 8, "slow": 21}, {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"}, ] """ name: str # = None # Required. ta: List = field(default_factory=list) # Required. # Helpful. More descriptive version or notes or w/e. description: str = "TA Description" # Optional. Gets Exchange Time and Local Time execution time created: str = get_time(to_string=True) def __post_init__(self): has_name = True is_ta = False required_args = ["[X] Strategy requires the following argument(s):"] name_is_str = isinstance(self.name, str) ta_is_list = isinstance(self.ta, list) if self.name is None or not name_is_str: required_args.append(' - name. Must be a string. Example: "My TA". Note: "all" is reserved.') has_name != has_name if self.ta is None: self.ta = None elif self.ta is not None and ta_is_list and self.total_ta() > 0: # Check that all elements of the list are dicts. # Does not check if the dicts values are valid indicator kwargs # User must check indicator documentation for all indicators args. is_ta = all([isinstance(_, dict) and len(_.keys()) > 0 for _ in self.ta]) else: s = " - ta. Format is a list of dicts. Example: [{'kind': 'sma', 'length': 10}]" s += "\n Check the indicator for the correct arguments if you receive this error." required_args.append(s) if len(required_args) > 1: [print(_) for _ in required_args] return None def total_ta(self): return len(self.ta) if self.ta is not None else 0 # All Default Strategy AllStrategy = Strategy( name="All", description="All the indicators with their default settings. Pandas TA default.", ta=None, ) # Default (Example) Strategy. CommonStrategy = Strategy( name="Common Price and Volume SMAs", description="Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.", ta=[ {"kind": "sma", "length": 10}, {"kind": "sma", "length": 20}, {"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}, {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOL"} ] ) # Base Class for extending a Pandas DataFrame class BasePandasObject(PandasObject): """Simple PandasObject Extension Ensures the DataFrame is not empty and has columns. It would be a sad Panda otherwise. Args: df (pd.DataFrame): Extends Pandas DataFrame """ def __init__(self, df, **kwargs): if df.empty: return if len(df.columns) > 0: common_names = { "Date": "date", "Time": "time", "Timestamp": "timestamp", "Datetime": "datetime", "Open": "open", "High": "high", "Low": "low", "Close": "close", "Adj Close": "adj_close", "Volume": "volume", "Dividends": "dividends", "Stock Splits": "split", } # Preemptively drop the rows that are all NaNs # Might need to be moved to AnalysisIndicators.__call__() to be # toggleable via kwargs. # df.dropna(axis=0, inplace=True) # Preemptively rename columns to lowercase df.rename(columns=common_names, errors="ignore", inplace=True) # Preemptively lowercase the index index_name = df.index.name if index_name is not None: df.index.rename(index_name.lower(), inplace=True) self._df = df else: raise AttributeError(f"[X] No columns!") def __call__(self, kind, *args, **kwargs): raise NotImplementedError() # Pandas TA - DataFrame Analysis Indicators @pd.api.extensions.register_dataframe_accessor("ta") class AnalysisIndicators(BasePandasObject): """ This Pandas Extension is named 'ta' for Technical Analysis. In other words, it is a Numerical Time Series Feature Generator where the Time Series data is biased towards Financial Market data; typical data includes columns named :"open", "high", "low", "close", "volume". This TA Library hopefully allows you to apply familiar and unique Technical Analysis Indicators easily with the DataFrame Extension named 'ta'. Even though 'ta' is a Pandas DataFrame Extension, you can still call Technical Analysis indicators individually if you are more comfortable with that approach or it allows you to easily and automatically apply the indicators with the strategy method. See: help(ta.strategy). By default, the 'ta' extension uses lower case column names: open, high, low, close, and volume. You can override the defaults by providing the it's replacement name when calling the indicator. For example, to call the indicator hl2(). With 'default' columns: open, high, low, close, and volume. >>> df.ta.hl2() >>> df.ta(kind="hl2") With DataFrame columns: Open, High, Low, Close, and Volume. >>> df.ta.hl2(high="High", low="Low") >>> df.ta(kind="hl2", high="High", low="Low") If you do not want to use a DataFrame Extension, just call it normally. >>> sma10 = ta.sma(df["Close"]) # Default length=10 >>> sma50 = ta.sma(df["Close"], length=50) >>> ichimoku, span = ta.ichimoku(df["High"], df["Low"], df["Close"]) Args: kind (str, optional): Default: None. Kind is the 'name' of the indicator. It converts kind to lowercase before calling. timed (bool, optional): Default: False. Curious about the execution speed? kwargs: Extension specific modifiers. append (bool, optional): Default: False. When True, it appends the resultant column(s) to the DataFrame. Returns: Most Indicators will return a Pandas Series. Others like MACD, BBANDS, KC, et al will return a Pandas DataFrame. Ichimoku on the other hand will return two DataFrames, the Ichimoku DataFrame for the known period and a Span DataFrame for the future of the Span values. Let's get started! 1. Loading the 'ta' module: >>> import pandas as pd >>> import ta as ta 2. Load some data: >>> df = pd.read