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Initial tests ok
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Urban Ottosson
+103
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@@ -2,131 +2,151 @@
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import os
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import sys
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from os.path import dirname, abspath, join
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from os.path import abspath, join, exists, basename, splitext
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from glob import glob
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import importlib
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import pandas_ta
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import pandas as pd
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def import_dir(dir_path, create_categories=True, verbose=True):
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def create_dir(dir_path, create_categories=True, verbose=True):
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"""
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Helper function to setup a suitable folder structure for working with
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custom indicators.
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Args:
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dir_path (str): Full path to where you want your indicator tree
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create_categories (bool): If True create category sub-folders
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verbose (bool): If True verbose output of results
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"""
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# ensure that the passed directory exists / is readable
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if not os.path.exists(dir_path):
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print(f"[X] Unable to read the directory '{dir_path}'.")
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return
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if not exists(dir_path):
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os.makedirs(dir_path)
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if verbose:
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print(f"[i] Created main directory '{dir_path}'.")
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# list the contents of the directory
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dirs = glob(abspath(join(dir_path, '*')))
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# optionally add any missing category subdirectories
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if create_categories:
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for sd in [*pandas_ta.Category]:
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d = abspath(join(dir_path, sd)
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if not os.path.exists(d):
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os.makedirs(directory
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d = abspath(join(dir_path, sd))
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if not exists(d):
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os.makedirs(d)
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if verbose:
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dirname = basename(d)
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print(f"[i] Created an empty sub-directory '{dirname}'.")
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# traverse the directory, importing all modules found there
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def import_dir(dir_path, verbose=True):
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# ensure that the passed directory exists / is readable
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if not exists(dir_path):
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print(f"[X] Unable to read the directory '{dir_path}'.")
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return
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# obtain a list of all reserved pandas_ta indicator names
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df = pd.DataFrame()
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names_already_in_use = df.ta.indicators(as_list=True)
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# list the contents of the directory
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dirs = glob(abspath(join(dir_path, '*')))
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# traverse full directory, importing all modules found there
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for d in dirs:
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dirname = os.path.basename(d)
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dirname = basename(d)
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# only look in directories which are valid pandas_ta categories
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if dirname not in [*pandas_ta.Category]:
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print(f"[i] Skipping the sub-directory '{dirname}' since it's not a pandas_ta category.")
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if verbose:
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print(f"[i] Skipping the sub-directory '{dirname}' since it's not a valid pandas_ta category.")
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continue
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# for each module found in that category (directory)...
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for module in glob(abspath(join(dir_path, dirname, '*.py'))):
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module = os.path.splitext(os.path.basename(module))[0]
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module = splitext(basename(module))[0]
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if module in pandas_ta.Category[dirname]:
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print(f"[X] Skipping the custom module '{module}' since a function with that name already exists in pandas_ta.")
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# check that we only load modules not already loaded in pandas_ta
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if module in names_already_in_use:
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print(f"[i] Warning: the custom module '{module}' will replace a module in pandas_ta.")
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continue
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# import the module and add it to the correct category
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pandas_ta.Category[dirname].append(module)
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# ensure that the supplied path is included in our python path
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if d not in sys.path:
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sys.path.append(d)
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# import the module and add it to the correct category
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importlib.import_module(module, d)
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pandas_ta.Category[dirname].append(module)
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if verbose:
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print(f"[i] Successfully imported the module '{module}' into category '{dirname}'.")
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print(f"[i] Successfully imported the indicator '{module}' into category '{dirname}'.")
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import_dir.__doc__ = \
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"""
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Import a directory of custom indicators into pandas_ta
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Args:
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dir_path (str): Full path to your indicator tree
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verbose (bool): If True verbose output of results
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This method allows you to experiment and develop your own technical analysis
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indicators independantly in a separate local directory of your choice but
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still use them seamlessly together with the existing pandas_ta functions just
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like if they were part of pandas_ta.
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indicators in a separate local directory of your choice but use them seamlessly
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together with the existing pandas_ta functions just like if they were part of
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pandas_ta.
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If you at some late point would like to push them into the pandas_ta library
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you can do so very easily by following the step by step instruction here
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https://github.com/twopirllc/pandas-ta/issues/264.
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----------------------------------
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By default, the 'ta' extension uses lower case column names: open, high,
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low, close, and volume. You can override the defaults by providing the it's
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replacement name when calling the indicator. For example, to call the
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indicator hl2().
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With 'default' columns: open, high, low, close, and volume.
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>>> df.ta.hl2()
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>>> df.ta(kind="hl2")
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With DataFrame columns: Open, High, Low, Close, and Volume.
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>>> df.ta.hl2(high="High", low="Low")
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>>> df.ta(kind="hl2", high="High", low="Low")
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If you do not want to use a DataFrame Extension, just call it normally.
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>>> sma10 = ta.sma(df["Close"]) # Default length=10
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>>> sma50 = ta.sma(df["Close"], length=50)
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>>> ichimoku, span = ta.ichimoku(df["High"], df["Low"], df["Close"])
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Args:
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kind (str, optional): Default: None. Kind is the 'name' of the indicator.
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It converts kind to lowercase before calling.
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timed (bool, optional): Default: False. Curious about the execution
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speed?
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kwargs: Extension specific modifiers.
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append (bool, optional): Default: False. When True, it appends the
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resultant column(s) to the DataFrame.
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Returns:
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Most Indicators will return a Pandas Series. Others like MACD, BBANDS,
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KC, et al will return a Pandas DataFrame. Ichimoku on the other hand
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will return two DataFrames, the Ichimoku DataFrame for the known period
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and a Span DataFrame for the future of the Span values.
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Let's get started!
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1. Loading the 'ta' module:
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>>> import pandas as pd
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>>> import ta as ta
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2. Load some data:
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2. Create an empty directory on your machine where you want to work with your
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indicators. Invoke pandas_ta.custom.import_dir once to pre-populate it with
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sub-folders for all available indicator categories, e.g.:
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>>> ta.custom.create_dir('~/my_indicators')
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3. You can now create your own custom indicator e.g. by copying existing
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ones from pandas_ta core module and modifying them. Each custom indicator
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should have a unique name and have both a function and a method defined.
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For an example of the correct structure, look at the example ni.py in the
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examples folder.
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The ni.py indicator is a trend indicator so we drop it into the sub-folder
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named trend. Thus we have a folder structure like this:
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~/my_indicators/
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│
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├── candles/
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.
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.
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└── trend/
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. └── ni.py
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.
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└── volume/
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4. We can now dynamically load all our custom indicators located in our
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designated indicators directory like this:
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>>> ta.custom.import_dir('~/my_indicators')
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If your custom indicator loaded succesfully then it should behave exactly
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like all other native indicators in pandas_ta. E.g.
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>>> help(ta.ni)
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>>> help(df.ta.ni)
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>>> df = pd.read_csv("AAPL.csv", index_col="date", parse_dates=True)
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3. Help!
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3a. General Help:
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>>> help(df.ta)
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>>> df.ta()
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3b. Indicator Help:
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>>> help(ta.apo)
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3c. Indicator Extension Help:
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>>> help(df.ta.apo)
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4. Ways of calling an indicator.
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4a. Standard: Calling just the APO indicator without "ta" DataFrame extension.
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>>> ta.apo(df["close"])
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4b. DataFrame Extension: Calling just the APO indicator with "ta" DataFrame extension.
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>>> df.ta.apo()
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4c. DataFrame Extension (kind): Calling APO using 'kind'
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>>> df.ta(kind="apo")
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4d. Strategy:
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>>> ta.ni(df["close"])
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>>> df.ta.ni()
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>>> df.ta(kind="ni")
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>>> df.ta.strategy("All") # Default
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>>> df.ta.strategy(ta.Strategy("My Strat", ta=[{"kind": "apo"}])) # Custom
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5. Working with kwargs
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5a. Append the result to the working df.
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>>> df.ta.apo(append=True)
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5b. Timing an indicator.
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>>> apo = df.ta(kind="apo", timed=True)
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>>> print(apo.timed)
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>>> df.ta.strategy(ta.Strategy("My Strat", ta=[{"kind": "ni"}])) # Custom
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>>> df.ta.ni(append=True)
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>>> ni = df.ta(kind="ni", timed=True)
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>>> print(ni.timed)
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"""
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