from algo import Algo
import plotly.graph_objects as go
from datetime import datetime

class Ema_Algo(Algo):
    def __init__(self, shortPeriod=5, longPeriod=13):
        self.shortPeriod = shortPeriod
        self.longPeriod = longPeriod
        self.g_data = {
            "timestamps" : [],
            "ema_short" : [],
            "ema_long" : []
        }

    @property
    def name(self):
        return "EMA Algo"
    
    @property
    def graph_data(self):
         return self.g_data
    
    def export_graph(self, g_data):
        timestamps = [datetime.fromtimestamp(t) for t in g_data['timestamps']]
        ema_5 = g_data['ema_short']
        ema_13 = g_data['ema_long']
        exp = [
            go.Scatter(name='5 day EMA', x=timestamps, y=ema_5, line=dict(color='rgb(0, 255, 255)'),  mode='lines'),
            go.Scatter(name='13 day EMA', x=timestamps, y=ema_13, line=dict(color='rgb(255, 0, 255)'),  mode='lines')
        ]
        return exp
    
    def detemine_signal(self, timestamps, prices):

        ema_5 = self.calc_emas(self.shortPeriod, prices)
        ema_13 = self.calc_emas(self.longPeriod, prices)

        # add to graph data
        self.graph_data["timestamps"].append(timestamps[-1])
        self.graph_data["ema_short"].append(ema_5[-1])
        self.graph_data["ema_long"].append(ema_13[-1])

        # determine the sign from the most recent price
        sign_signal = ema_5[-1] - ema_13[-1]
        # current position, (liquid, shares)
        if sign_signal > 0:
                return 1.0 # buy max shares
        if sign_signal < 0:
                return 0.0 # sell all shares
        
        return 0.5

    """
    Calculates the simple moving average of the first period of the data
    """
    def calc_first_sma(self, 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
    
    """
    Returns an array off all EMAs, computed according to period
    """
    def calc_emas(self, period, prices):
        weighted_multiplier = 2.0 / (period + 1.0)

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

        # calculate the rest ema's using that first
        emas = [first_ema] * period # 0, 1, 2 (for period 3)
        for i in range(period, len(prices)): # 3, 4, 5, 6, ... , last
            last_ema = emas[-1]
            if prices[i] == None or prices[i] == 0:
                print(i)
            next_ema = prices[i] * weighted_multiplier + last_ema * (1 - weighted_multiplier)
            emas.append(next_ema)
        return emas