from collections import namedtuple

import csv

from datetime import datetime

import itertools as it

import functools as ft

class DataPoint(namedtuple('DataPoint', ['date', 'value'])):

__slots__ = ()

def __le__(self, other):

return self.value <= other.value

def __lt__(self, other):

return self.value < other.value

def __gt__(self, other):

return self.value > other.value

def consecutive_positives(sequence, zero=0):

def _consecutives():

for itr in it.repeat(iter(sequence)):

yield tuple(it.takewhile(lambda p: p > zero,

it.dropwhile(lambda p: p <= zero, itr)))

return it.takewhile(lambda t: len(t), _consecutives())

def read_prices(csvfile, _strptime=datetime.strptime):

with open(csvfile) as infile:

reader = csv.DictReader(infile)

for row in reader:

yield DataPoint(date=_strptime(row['Date'], '%Y-%m-%d').date(),

value=float(row['Adj Close']))

# Read prices and calculate daily percent change.

prices = tuple(read_prices('SP500.csv'))

gains = tuple(DataPoint(day.date, 100*(day.value/prev_day.value - 1.))

for day, prev_day in zip(prices[1:], prices))

# Find maximum daily gain/loss.

zdp = DataPoint(None, 0) # zero DataPoint

max_gain = ft.reduce(max, it.filterfalse(lambda p: p <= zdp, gains))

max_loss = ft.reduce(min, it.filterfalse(lambda p: p > zdp, gains), zdp)

# Find longest growth streak.

growth_streaks = consecutive_positives(gains, zero=DataPoint(None, 0))

longest_streak = ft.reduce(lambda x, y: x if len(x) > len(y) else y,

growth_streaks)

# Display results.

print('Max gain: {1:.2f}% on {0}'.format(*max_gain))

print('Max loss: {1:.2f}% on {0}'.format(*max_loss))

print('Longest growth streak: {num_days} days ({first} to {last})'.format(

num_days=len(longest_streak),

first=longest_streak[0].date,

last=longest_streak[-1].date

))