import random
import numpy as np
import talib


class Indicator:
    def __init__(self, name, indicator_type, properties):
        # Initialise all indicators with some default properties.
        self.name = name
        self.properties = properties
        self.properties['type'] = indicator_type
        if 'value' not in properties:
            self.properties['value'] = 0
        if 'visible' not in properties:
            self.properties['visible'] = True


class Volume(Indicator):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)

    def calculate(self, candles, num_results=800):
        return candles.get_volume(self.properties['type'])


class SMA(Indicator):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)
        if 'color' not in properties:
            self.properties['color'] = f"#{random.randrange(0x1000000):06x}"
        if 'period' not in properties:
            self.properties['period'] = 20

    def calculate(self, candles, num_results=800):
        # These indicators do computations over period number of price data points.
        # So we need at least that plus what ever amount of results needed.
        num_cv = self.properties['period'] + num_results
        closing_data = candles.get_latest_close_values(num_cv)
        if len(closing_data) < num_cv:
            print(f'Could not calculate {self.properties["type"]} for time period of {self.properties["period"]}')
            print('Not enough data available')
            return
        # Initialize two arrays to hold a list of closing values and
        # a list of timestamps associated with these values
        closes = []
        ts = []
        # Isolate all the closing values and timestamps from
        # the dictionary object
        for each in closing_data:
            closes.append(each['close'])
            ts.append(each['time'])
        # Convert the list of closes to a numpy array
        np_real_data = np.array(closes, dtype=float)
        # Pass the closing values and the period parameter to talib
        i_values = None
        if self.properties['type'] == 'SMA':
            i_values = talib.SMA(np_real_data, self.properties['period'])
        if self.properties['type'] == 'RSI':
            i_values = talib.RSI(np_real_data, self.properties['period'])
        if self.properties['type'] == 'EMA':
            i_values = talib.EMA(np_real_data, self.properties['period'])
        if self.properties['type'] == 'LREG':
            i_values = talib.LINEARREG(np_real_data, self.properties['period'])

        # Combine the new data with the timestamps
        # Warning: The first <period> of rsi values are <NAN>.
        # But they should get trimmed off todo get rid of try except *just debugging info
        try:
            i_values = i_values[-num_results:]
        except Exception:
            raise ValueError(f'error: {self.properties.type} {i_values}')
        ts = ts[-num_results:]
        r_data = []
        for each in range(len(i_values)):
            r_data.append({'time': ts[each], 'value': i_values[each]})
        return {"type": self.properties['type'], "data": r_data}


class EMA(SMA):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)


class RSI(SMA):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)


class LREG(SMA):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)


class ATR(SMA):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)

    def calculate(self, candles, num_results=800):
        # These indicators do computations over period number of price data points.
        # So we need at least that plus what ever amount of results needed.
        num_cv = self.properties.period + num_results

        high_data = candles.get_latest_high_values(num_cv)
        low_data = candles.get_latest_low_values(num_cv)
        close_data = candles.get_latest_close_values(num_cv)
        if len(close_data) < num_cv:
            print(f'Couldn\'t calculate {self.properties.type} for time period of {self.properties.period}')
            print('Not enough data availiable')
            return
        # Initialize 4 arrays to hold a list of h/l/c values and
        # a list of timestamps associated with these values
        highs = []
        lows = []
        closes = []
        ts = []
        # Isolate all the values and timestamps from
        # the dictionary objects

        for each in high_data:
            highs.append(each['high'])
        for each in low_data:
            lows.append(each['low'])
        for each in close_data:
            closes.append(each['close'])
            ts.append(each['time'])
        # Convert the lists to a numpy array
        np_highs = np.array(highs, dtype=float)
        np_lows = np.array(lows, dtype=float)
        np_closes = np.array(closes, dtype=float)
        # Pass the closing values and the period parameter to talib
        atr = talib.ATR(high=np_highs,
                        low=np_lows,
                        close=np_closes,
                        timeperiod=self.properties.period)
        # Combine the new data with the timestamps
        # Warning: The first (<period> -1) of values are <NAN>.
        # But they should get trimmed off
        atr = atr[-num_results:]
        ts = ts[-num_results:]
        r_data = []
        for each in range(len(atr)):
            # filter out nan values
            if np.isnan(atr[each]):
                continue
            r_data.append({'time': ts[each], 'value': atr[each]})
        return {"type": self.properties.type, "data": r_data}


class BolBands(Indicator):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)
        ul_col = f"#{random.randrange(0x1000000):06x}"
        if 'period' not in properties:
            self.properties['period'] = 50
        if 'color_1' not in properties:
            self.properties['color_1'] = ul_col
        if 'color_2' not in properties:
            self.properties['color_2'] = f"#{random.randrange(0x1000000):06x}"
        if 'color_3' not in properties:
            self.properties['color_3'] = ul_col
        if 'value1' not in properties:
            self.properties['value1'] = 0
        if 'value2' not in properties:
            self.properties['value2'] = 0
        if 'value3' not in properties:
            self.properties['value3'] = 0
        if 'devup' not in properties:
            self.properties['devup'] = 2
        if 'devdn' not in properties:
            self.properties['devdn'] = 2
        if 'ma' not in properties:
            self.properties['ma'] = 1

    def calculate(self, candles, num_results=800):
        # These indicators do computations over period number of price data points.
        # So we need at least that plus what ever amount of results needed.
        # Acceptable values for ma in the talib.BBANDS
        # {'SMA':0,'EMA':1, 'WMA' : 2, 'DEMA' : 3, 'TEMA' : 4, 'TRIMA' : 5, 'KAMA' : 6, 'MAMA' : 7, 'T3' : 8}
        num_cv = self.properties['period'] + num_results
        closing_data = candles.get_latest_close_values(num_cv)
        if len(closing_data) < num_cv:
            print(f'Couldn\'t calculate {self.properties["type"]} for time period of {self.properties["period"]}')
            print('Not enough data availiable')
            return
        # Initialize two arrays to hold a list of closing values and
        # a list of timestamps associated with these values
        closes = []
        ts = []
        # Isolate all the closing values and timestamps from
        # the dictionary object
        for each in closing_data:
            closes.append(each['close'])
            ts.append(each['time'])
        # Convert the list of closes to a numpy array
        np_real_data = np.array(closes, dtype=float)
        # Pass the closing values and the period parameter to talib
        upper, middle, lower = talib.BBANDS(np_real_data,
                                            timeperiod=self.properties['period'],
                                            # number of non-biased standard deviations from the mean
                                            nbdevup=self.properties['devup'],
                                            nbdevdn=self.properties['devdn'],
                                            # Moving average type: simple moving average here
                                            matype=self.properties['ma'])

        # Combine the new data with the timestamps
        # Warning: The first (<period> -1) of values are <NAN>.
        # But they should get trimmed off
        i_values_u = upper[-num_results:]
        i_values_m = middle[-num_results:]
        i_values_l = lower[-num_results:]
        ts = ts[-num_results:]
        r_data_u = []
        r_data_m = []
        r_data_l = []
        for each in range(len(i_values_u)):
            # filter out nan values
            if np.isnan(i_values_u[each]):
                continue
            r_data_u.append({'time': ts[each], 'value': i_values_u[each]})
            r_data_m.append({'time': ts[each], 'value': i_values_m[each]})
            r_data_l.append({'time': ts[each], 'value': i_values_l[each]})
        r_data = [r_data_u, r_data_m, r_data_l]
        return {"type": self.properties['type'], "data": r_data}


class MACD(Indicator):
    def __init__(self, name, indicator_type, properties):
        super().__init__(name, indicator_type, properties)
        if 'fast_p' not in properties:
            self.properties['fast_p'] = 12
        if 'slow_p' not in properties:
            self.properties['slow_p'] = 26
        if 'signal_p' not in properties:
            self.properties['signal_p'] = 9
        if 'macd' not in properties:
            self.properties['macd'] = 0
        if 'signal' not in properties:
            self.properties['signal'] = 0
        if 'hist' not in properties:
            self.properties['hist'] = 0
        if 'color_1' not in properties:
            self.properties['color_1'] = f"#{random.randrange(0x1000000):06x}"
        if 'color_2' not in properties:
            self.properties['color_2'] = f"#{random.randrange(0x1000000):06x}"

    def calculate(self, candles, num_results=800):
        # These indicators do computations over a period number of price data points.
        # So we need at least that plus what ever amount of results needed.
        # It seems it needs num_of_nans = (slow_p) - 2) + signal_p

        # TODO: slow_p or fast_p which ever is greater should be used in the calc below.
        # TODO but i am investigating this.
        if self.properties['fast_p'] > self.properties['slow_p']:
            raise ValueError('Error I think: TODO: calculate_macd()')
        num_cv = (self.properties['slow_p'] - 2) + self.properties['signal_p'] + num_results

        closing_data = candles.get_latest_close_values(num_cv)
        if len(closing_data) < num_cv:
            print(f'Couldn\'t calculate {self.properties["type"]} for time period of {self.properties["slow_p"]}')
            print('Not enough data available')
            return
        # Initialize two arrays to hold a list of closing values and
        # a list of timestamps associated with these values
        closes = []
        ts = []
        # Isolate all the closing values and timestamps from
        # the dictionary object
        for each in closing_data:
            closes.append(each['close'])
            ts.append(each['time'])
        # Convert the list of closes to a numpy array
        np_real_data = np.array(closes, dtype=float)
        # Pass the closing values and the period parameter to talib
        macd, signal, hist = talib.MACD(
            np_real_data,
            self.properties['fast_p'],
            self.properties['slow_p'],
            self.properties['signal_p'])

        # Combine the new data with the timestamps
        # Warning: The first (<period> -1) of values are <NAN>.
        # But they should get trimmed off
        macd = macd[-num_results:]
        if len(macd) == 1:
            print('looks like after slicing')
            print(macd)
        signal = signal[-num_results:]
        hist = hist[-num_results:]
        ts = ts[-num_results:]
        r_macd = []
        r_signal = []
        r_hist = []
        for each in range(len(macd)):
            # filter out nan values
            if np.isnan(macd[each]):
                continue
            r_macd.append({'time': ts[each], 'value': macd[each]})
            r_signal.append({'time': ts[each], 'value': signal[each]})
            r_hist.append({'time': ts[each], 'value': hist[each]})
        r_data = [r_macd, r_signal, r_hist]
        return {"type": self.properties['type'], "data": r_data}


class Indicators:
    def __init__(self, candles, config):
        # Object containing Price and candle data.
        self.candles = candles

        # A reference to a dictionary of indicators and properties.
        # This is updated when a new indicator is created, so it's state should save.
        self.indicator_list = config.indicator_list
        # Dictionary of indicators objects
        self.indicators = {}
        # This fill above two dicts
        self.create_loaded_indicators()

        # Create a List of all available indicator types
        self.indicator_types = [ ]
        for i in self.indicator_list:
            if self.indicator_list[i]['type'] in self.indicator_types:
                continue
            self.indicator_types.append(self.indicator_list[i]['type'])

        # A list of values to use with bolenger bands
        self.bb_ma_val = {'SMA': 0, 'EMA': 1, 'WMA': 2, 'DEMA': 3, 'TEMA': 4, 'TRIMA': 5, 'KAMA': 6, 'MAMA': 7, 'T3': 8}

    def create_loaded_indicators(self):
        for each in self.indicator_list:
            self.create_indicator(each, self.indicator_list[each]['type'], self.indicator_list[each])
    @staticmethod
    def get_indicator_defaults():
        """Set the default settings for each indicator"""

        indicator_list = {
            'SMA 21': {'type': 'SMA', 'period': 21, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                       'value': 0},
            'EMA 50': {'type': 'EMA', 'period': 50, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                       'value': 0},
            'EMA 100': {'type': 'EMA', 'period': 100, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                        'value': 0},
            'SMA 200': {'type': 'SMA', 'period': 200, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                        'value': 0},
            'RSI 14': {'type': 'RSI', 'period': 14, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                       'value': 0},
            'RSI 8': {'type': 'RSI', 'period': 8, 'visible': True, 'color': f"#{random.randrange(0x1000000):06x}",
                      'value': 0},
            'Bolenger': {'color_1': '#5ad858', 'color_2': '#f0f664', 'color_3': '#5ad858', 'devdn': 2, 'devup': 2,
                         'ma': 1, 'period': 20, 'type': 'BOLBands', 'value': 0, 'value1': '38691.58',
                         'value2': '38552.36',
                         'value3': '38413.14', 'visible': 'True'},
            'vol': {'type': 'Volume', 'visible': True, 'value': 0}
        }
        return indicator_list

    def get_indicator_list(self):
        # Returns a list of all the indicator object in this class instance
        if not self.indicator_list:
            raise ValueError('get_indicator_list(): No indicators in the list')
        return self.indicator_list

    def get_enabled_indicators(self):
        """   Loop through all indicators and return a list of indicators marked visible  """
        enabled_indicators = []
        i_list = self.get_indicator_list()
        for indctr in i_list:
            if i_list[indctr]['visible']:
                enabled_indicators.append(indctr)
        return enabled_indicators

    def get_indicator_data(self, symbol=None, interval=None, num_results=800):
        # Loop through all the indicators. If enabled, run the appropriate
        # update function. Return all the results as a dictionary object.
        if symbol is not None:
            print(symbol)
            print('get_indicator_data() no symbol implementation')
        if interval is not None:
            print(interval)
            print('get_indicator_data() no interval implementation')
        # Get a list of indicator objects and a list of enabled indicators names.
        i_list = self.indicators
        enabled_i = self.get_enabled_indicators()
        result = {}
        # Loop through all indicator objects in i_list
        for each_i in i_list:
            # If the indicator's not enabled skip to next each_i
            if each_i not in enabled_i:
                continue
            result[each_i] = i_list[each_i].calculate(self.candles, num_results)
        return result

    def delete_indicator(self, indicator):
        del self.indicator_list[indicator]
        del self.indicators[indicator]

    def create_indicator(self, name, itype, properties):
        if itype == 'SMA':
            self.indicators[name] = SMA(name, itype, properties)
        if itype == 'EMA':
            self.indicators[name] = EMA(name, itype, properties)
        if itype == 'RSI':
            self.indicators[name] = RSI(name, itype, properties)
        if itype == 'LREG':
            self.indicators[name] = LREG(name, itype, properties)
        if itype == 'ATR':
            self.indicators[name] = ATR(name, itype, properties)
        if itype == 'BOLBands':
            self.indicators[name] = BolBands(name, itype, properties)
        if itype == 'MACD':
            self.indicators[name] = MACD(name, itype, properties)
        if itype == 'Volume':
            self.indicators[name] = Volume(name, itype, properties)

        if name not in self.indicator_list:
            # If we are loading from file it would already exist in here before creation.
            self.indicator_list[name] = properties
        return

    def update_indicators(self):
        return self.get_indicator_data(self, num_results=1)