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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)
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