Source code for ffn.utils

import re

import decorator
import numpy as np
import pandas as pd

    import cPickle as pickle
except ImportError:
    import pickle

def _memoize(func, *args, **kw):
    # should we refresh the cache?
    refresh = False
    refresh_kw = func.mrefresh_keyword

    # kw is not always set - check args
    if refresh_kw in func.__code__.co_varnames:
        if args[func.__code__.co_varnames.index(refresh_kw)]:
            refresh = True

    # check in kw if not already set above
    if not refresh and refresh_kw in kw:
        if kw[refresh_kw]:
            refresh = True

    key = pickle.dumps(args, 1) + pickle.dumps(kw, 1)

    cache = func.mcache
    if not refresh and key in cache:
        return cache[key]
        cache[key] = result = func(*args, **kw)
        return result

[docs]def memoize(f, refresh_keyword="mrefresh"): """ Memoize decorator. The refresh keyword is the keyword used to bypass the cache (in the function call). """ f.mcache = {} f.mrefresh_keyword = refresh_keyword return decorator.decorator(_memoize, f)
[docs]def parse_arg(arg): """ Parses arguments for convenience. Argument can be a csv list ('a,b,c'), a string, a list, a tuple. Returns a list. """ # handle string input if type(arg) == str: arg = arg.strip() # parse csv as tickers and create children if "," in arg: arg = arg.split(",") arg = [x.strip() for x in arg] # assume single string - create single item list else: arg = [arg] return arg
[docs]def clean_ticker(ticker): """ Cleans a ticker for easier use throughout MoneyTree Splits by space and only keeps first bit. Also removes any characters that are not letters. Returns as lowercase. >>> clean_ticker('^VIX') 'vix' >>> clean_ticker('SPX Index') 'spx' """ pattern = re.compile("[\\W_]+") res = pattern.sub("", ticker.split(" ")[0]) return res.lower()
[docs]def clean_tickers(tickers): """ Maps clean_ticker over tickers. """ return [clean_ticker(x) for x in tickers]
[docs]def fmtp(number): """ Formatting helper - percent """ if np.isnan(number): return "-" return format(number, ".2%")
[docs]def fmtpn(number): """ Formatting helper - percent no % sign """ if np.isnan(number): return "-" return format(number * 100, ".2f")
[docs]def fmtn(number): """ Formatting helper - float """ if np.isnan(number): return "-" return format(number, ".2f")
[docs]def get_freq_name(period): period = period.upper() periods = { "B": "business day", "C": "custom business day", "D": "daily", "W": "weekly", "M": "monthly", "BM": "business month end", "CBM": "custom business month end", "MS": "month start", "BMS": "business month start", "CBMS": "custom business month start", "Q": "quarterly", "BQ": "business quarter end", "QS": "quarter start", "BQS": "business quarter start", "Y": "yearly", "A": "yearly", "BA": "business year end", "AS": "year start", "BAS": "business year start", "H": "hourly", "T": "minutely", "S": "secondly", "L": "milliseonds", "U": "microseconds", } if period in periods: return periods[period] else: return None
[docs]def scale(val, src, dst): """ Scale value from src range to dst range. If value outside bounds, it is clipped and set to the low or high bound of dst. Ex: scale(0, (0.0, 99.0), (-1.0, 1.0)) == -1.0 scale(-5, (0.0, 99.0), (-1.0, 1.0)) == -1.0 """ if val < src[0]: return dst[0] if val > src[1]: return dst[1] return ((val - src[0]) / (src[1] - src[0])) * (dst[1] - dst[0]) + dst[0]
[docs]def as_percent(self, digits=2): return as_format(self, ".%s%%" % digits)
[docs]def as_format(item, format_str=".2f"): """ Map a format string over a pandas object. """ if isinstance(item, pd.Series): return x: format(x, format_str)) elif isinstance(item, pd.DataFrame): return item.applymap(lambda x: format(x, format_str))