import csv
import functools as ft
import itertools as it
from collections import namedtuple
from datetime import datetime


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 - 1.0))
    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,
    )
)