import json
import datetime

# pull stock data from json files
# timestamps_file = open('timestamps.json', 'r')
# timestamps_file_data = timestamps_file.read()
# timestamps = json.loads(timestamps_file_data)
# timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps]

# prices_file = open('close_prices.json', 'r')
# prices = json.loads(prices_file.read())

# print('timestamps:\t', timestamps, '\nprices:\t', prices)

# make the line data for the 5 day exponential moving average (EMA)

def calc_first_sma(period, prices):
    prices_sum = 0
    for i in range(0, period):
        prices_sum += prices[i] # 0, 1, 2, 3 ("popping" order)
    # print('prices_sum:\t', prices_sum)

    return prices_sum / period

def calc_emas(period, prices):
    weighted_multiplier = 2.0 / (period + 1.0)

    # calculate the first ema
    first_ema = calc_first_sma(period, prices)

    # calculate the rest ema's using that first
    emas = [first_ema] * period
    for i in range(period + 1, len(prices)): # 4, 5, 6, ... , last
        last_ema = emas[-1]
        next_ema = prices[i] * weighted_multiplier + last_ema * (1 - weighted_multiplier)
        emas.append(next_ema)
    return emas

def interpolate_intersection(intersection_indices, timestamps, prices1, prices2):
    left_index = intersection_indices[0]
    right_index = intersection_indices[1]
    if right_index == -1:
        return timestamps[left_index]
    
    y_1 = prices1[left_index]
    y_2 = prices1[right_index] # first line

    v_1 = prices2[left_index]
    v_2 = prices2[right_index] # second line

    x_1 = 0 # take this as zero the simplify the algebra
    x_diff = timestamps[right_index] - timestamps[left_index] # same for both lines

    # find intersection between those lines
    x_diff = x_diff.total_seconds()
    m_1 = (y_2 - y_1) / x_diff # slope of line 1
    m_2 = (v_2 - v_1) / x_diff

    x_interpolated = (v_1 - y_1) / (m_1 - m_2)
    y_interpolated = m_1 * (x_interpolated) + y_1

    # add back the time we subtracted to make x_1=0
    x_interpolated = datetime.timedelta(seconds = x_interpolated) + timestamps[left_index]
    return (x_interpolated, y_interpolated)



"""
Returns the indices of where two arrays' values intersects
"""
def find_intersections(prices1, prices2, offset=0):
    if len(prices1) != len(prices2):
        print("ERROR IN find_intersections: len of arrs not the same")
        return []
    prev_p1 = prices1[0]
    prev_p2 = prices2[0]
    intersection_indices = set()
    for i in range(1 + offset, len(prices1)):
        next_p1 = prices1[i]
        next_p2 = prices2[i]
        # if the sign (negative to positive) changes, then there was an intersection between these pts
        sub_prev = prev_p1 - prev_p2
        sub_next = next_p1 - next_p2

        if (sub_prev > 0 and sub_next < 0) or (sub_prev < 0 and sub_next > 0):
            intersection_indices.add((i-1, i))
        
        if sub_next == 0:
            intersection_indices.add((i, -1))

        prev_p1 = next_p1
        prev_p2 = next_p2
    
    return intersection_indices

def calculate_profit(buy_line, sell_line, prices, timestamps, offset=0, starting_money=10000):
    if len(buy_line) != len(sell_line):
        print("ERROR IN find_intersections: len of arrs not the same")
        return []
    is_bought = False
    curr_money = 10000
    shares_owned = 0
    buy_info = []
    sell_info = []
    for i in range(offset, len(buy_line)):
        current_b1 = buy_line[i]
        current_sl = sell_line[i]
        # if the sign is positive, we want to hold, if it's negative, we want to sell
        sign_signal = current_b1 - current_sl

        if sign_signal > 0:
            if not is_bought:
                # buy the stock
                shares_owned = curr_money / prices[i]
                curr_money = 0
                buy_info.append((timestamps[i], prices[i], i))
            is_bought = True
        if sign_signal < 0:
            if is_bought:
                # selling the stock
                curr_money = prices[i] * shares_owned
                shares_owned = 0
                sell_info.append((timestamps[i], prices[i], i))
            is_bought = False

    # TODO: consider end interval
    total_assets = prices[-1] * shares_owned + curr_money
    percent_gain = (total_assets - starting_money) / starting_money
    return (percent_gain, total_assets, buy_info, sell_info)