from math import nan
import pandas as pd
import plotly.graph_objects as go
from plotly import subplots
import os
import sqlite3

from hts.HTS import HTS
from stock.util.Stock2Vector import Stock2Vector
from stock.util.LabelChecker import LabelChecker

from hts.BuySellChecker import BuySellChecker

class Simulation (HTS):
    stock2Vector = None
    buySellChecker = None

    def __init__(self, RESOURCE_PATH):
        super().__init__(RESOURCE_PATH)

        self.RESOURCE_PATH = RESOURCE_PATH

        self.buySellChecker = BuySellChecker()

        try:
            self.stock2Vector = Stock2Vector(RESOURCE_PATH)
            self.labelChecker = LabelChecker(RESOURCE_PATH)
        except:
            pass
        #self.connect()
        return

    def draw(self, stock_code, given_day, data, bsLine):
        if bsLine is None:
            return

        # 어제 데이터는 지운다.
        data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
        buy_line = bsLine['buy'][len(bsLine['buy'])-len(data):]
        buy_weight_line = bsLine['buy_weight'][len(bsLine['buy'])-len(data):]
        sell_line = bsLine['sell'][len(bsLine['buy'])-len(data):]

        # 그래프 설정을 위한 변수를 생성한다.
        data = data.astype({'open': 'int', 'high': 'int', 'low': 'int', 'close': 'int', 'volume': 'int',
                            'avg5': 'float', 'avg20': 'float', 'avg60': 'float', 'avg120': 'float', 'avg200': 'float',
                            'disparity_avg5': 'float', 'disparity_avg20': 'float', 'disparity_avg60': 'float', 'disparity_avg120': 'float', 'disparity_avg200': 'float',
                            'fast_k': 'float', 'slow_k': 'float', 'slow_d': 'float',
                            'macd': 'float', 'macds': 'float', 'macdo': 'float',
                            'rsi': 'float', 'rsis': 'float'
                            })

        buy_size = []
        buy_colors = []
        for i in range(len(buy_line)):
            if buy_line[i] < 0:
                buy_colors.append("#ffffff")
                buy_line[i] = nan
                buy_size.append(0)
            else:
                buy_colors.append("#0C752E")
                buy_size.append(10 + (5 * buy_weight_line[i]))

        sell_colors = []
        for i in range(len(sell_line)):
            if sell_line[i] < 0:
                sell_colors.append("#ffffff")
                sell_line[i] = nan
            else:
                sell_colors.append("#00ced1")

        volume_colors = []
        for i in range(len(buy_line)):
            if data['open'][i] > data['close'][i]:
                volume_colors.append("#0000FF")
            elif data['open'][i] < data['close'][i]:
                volume_colors.append("#FF0000")
            else:
                volume_colors.append("#000000")

        # 그래프를 설정한다.
        buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
        sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))

        #volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
        volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color=volume_colors, name='volume')

        disparity_avg5 = go.Scatter(x=data['date'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#F81191')
        disparity_avg20 = go.Scatter(x=data['date'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#097F19')
        disparity_avg30 = go.Scatter(x=data['date'], y=data["disparity_avg30"], name="disparity_avg30", line_color='#097F19')
        disparity_avg60 = go.Scatter(x=data['date'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#671BEA')
        disparity_avg120 = go.Scatter(x=data['date'], y=data["disparity_avg120"], name="disparity_avg120", line_color='#DFB809')
        disparity_avg200 = go.Scatter(x=data['date'], y=data["disparity_avg200"], name="disparity_avg200", line_color='#000000')

        macd_line = go.Scatter(x=data['date'], y=data["macd"], line=dict(color='red', width=2), name='macd')
        macd_s_line = go.Scatter(x=data['date'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
        macd_o_line = go.Bar(x=data['date'], y=data["macdo"], marker_color='purple', name='macdo')

        # fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
        slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
        slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')

        rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
        rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')

        upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
        lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')

        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#F81191')
        avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#097F19')
        avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#097F19')
        avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#671BEA')
        avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#DFB809')
        avg200 = go.Scatter(x=data['date'], y=data["avg200"], name="avg200", line_color='#000000')

        changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200') #CF6E0D
        baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='orange')
        laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
        leadingSpan1 = go.Scatter(x=data['date'], y=data["leadingSpan1"], name='leadingSpan1', line_color='black')
        leadingSpan2 = go.Scatter(x=data['date'], y=data["leadingSpan2"], name='leadingSpan2', line_color='black')

        text_list = []
        for i in range(len(data['macd'])):
            text = "{}<br><br>open: {}<br>close: {}<br>high: {}<br>low: {}<br>volume: {}<br><br>avg5: {:.2f}<br>avg20: {:.2f}<br>avg60: {:.2f}<br>avg200: {:.2f}<br><br>d_avg5: {:.6f}<br>d_avg20: {:.6f}<br>d_avg60: {:.6f}<br>d_avg200: {:.6f}<br>d_avg5_60: {:.6f}<br>d_avg5_200: {:.6f}<br>d_avg200-5: {:.6f}<br><br>macd: {:.2f}<br>slow_k: {:.2f}<br>rsi: {:.2f}".format(
                data['date'][i],
                data['open'][i],data['close'][i],data['high'][i],data['low'][i],data['volume'][i],
                data['avg5'][i],data['avg20'][i],data['avg60'][i],data['avg200'][i],
                data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i],
                max(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i]) - min(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i]),
                max(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i]) - min(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i]),
                abs(data['disparity_avg200'][i] - data['disparity_avg5'][i]),
                data['macd'][i],data['slow_k'][i],data['rsi'][i]
            )
            text_list.append(text)

        candle_stick = go.Candlestick(x=data['date'],
                                      open=data['open'],
                                      high=data['high'],
                                      low=data['low'],
                                      close=data['close'],
                                      increasing_line_color='red',
                                      decreasing_line_color='blue',
                                      showlegend=False,
                                      text=text_list,
                                      hoverinfo="text"
                                      )

        # candle_data = [avg5, avg20, avg30, avg60, avg120, avg200, buy_check, sell_check, laggingSpan, changeLine, baseLine, upper, lower, candle_stick]
        # candle_data = [avg5, avg20, avg30, avg60, avg200, buy_check, sell_check, upper, lower, candle_stick]
        # candle_data = [buy_check, sell_check, changeLine, baseLine, changeLine, laggingSpan, candle_stick]
        candle_data = [avg5, avg200, buy_check, sell_check, candle_stick]

        volume_data = [volume_line]
        disparity_data = [disparity_avg5, disparity_avg20, disparity_avg30, disparity_avg60, disparity_avg120, disparity_avg200]
        macd_data = [macd_line, macd_s_line, macd_o_line]
        stochastic_data = [slow_k_line, slow_d_line]
        rsi_data = [rsi_line, rsis_line]

        # 그래프를 그린다.
        """
        fig = go.Figure(data=candle_data)
        fig.update_layout(title=stock_code + "_" + given_day)
        fig.show()
        """

        fig = subplots.make_subplots(
            rows=6, cols=1,
            subplot_titles=("거래량", "이격도", "스토캐스틱", "RSI", "MACD", '캔들'),
            #specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
            shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
            row_heights=[200, 200, 200, 200, 200, 700]
        )
        for trace in volume_data:
            fig.append_trace(trace, 1, 1)
        for trace in disparity_data:
            fig.append_trace(trace, 2, 1)
        for trace in stochastic_data:
            fig.append_trace(trace, 3, 1)
        for trace in rsi_data:
            fig.append_trace(trace, 4, 1)
        for trace in macd_data:
            fig.append_trace(trace, 5, 1)
        for trace in candle_data:
            fig.append_trace(trace, 6, 1)

        #fig.update_xaxes(nticks=5)
        #fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)

        df = pd.DataFrame(bsLine)
        df = df.fillna(-1)
        buy_count = len(df.loc[df["buy"] > 0])
        sell_count = len(df.loc[df["sell"] > 0])

        fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
        #fig.update_layout(title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
        fig.show()

        return


    def makeTickData(self, data, mins=30):
        result = {"check": set(),
                  "time": [],
                  "open": [],
                  "close": [],
                  "high": [],
                  "low": [],
                  "vol": [],
                  "label": []}

        for i in range(mins, len(data['time'])+1):
            result["check"].add(data['time'][i-1])
            result["time"].append(data['time'][i-1])

            result["open"].append(data['open'][i-mins])
            result["close"].append(data['close'][i-1])
            result["high"].append(max(data['high'][i - mins: i]))
            result["low"].append(min(data['low'][i - mins: i]))
            result["vol"].append(sum(data['vol'][i - mins: i]))

        return result

    def simulate(self, stock, analyzed_day=1000):
        stock_code = stock['code']
        for ymd in stock['ymd']:
            LAST_DATA = self.stock2Vector.getLastData(stock_code, ymd)
            # 1분봉
            result = self.stock2Vector.getRealTime(stock_code, ymd, LAST_DATA)
            # 5분봉
            #result = self.makeTickData(result, mins=5)
            # 30분봉
            #result = self.makeTickData(result, mins=30)

            data = self.buySellChecker.analyze(result)
            data.drop(data.index[:len(data) - analyzed_day], inplace=True)

            # 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
            #data_5 = self.buySellChecker.analyze(result_5)
            # 분석일 데이터만 활용한다 (이전 데이터는 제거)
            #data_5.drop(data_5.index[:len(data_5) - analyzed_day], inplace=True)

            #data_30 = self.buySellChecker.analyze(result_30)
            # 분석일 데이터만 활용한다 (이전 데이터는 제거)
            #data_30.drop(data_30.index[:len(data_30) - analyzed_day], inplace=True)

            # 사야 할 시점과 팔아야 할 시점을 체크한다.
            #bsLine = self.buySellChecker.checkTransaction(stock_code, data, data_5, data_30, isRealTime=False)

            # 어제 데이터는 지운다.
            #data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
            bsLine = self.buySellChecker.checkTransaction(data, isRealTime=False)

            # 그래프를 그린다.
            self.draw(stock_code, ymd, data, bsLine)
        return

if __name__ == "__main__":

    PROJECT_HOME = os.getcwd()
    RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")

    #day_list = ['20231016', '20231017', '20231018', '20231019', '20231020', '20231023', '20231024', '20231025', '20231026', '20231027', '20231030', '20231031', '20231101', '20231102']
    day_list = ['20231113']
    stocks = [
        {'code': '252670', 'name': 'KODEX 200선물인버스2X', 'ymd': day_list},
        {'code': '122630', 'name': 'KODEX 레버리지', 'ymd': day_list},
        {'code': '233740', 'name': 'KODEX 코스닥150레버리지', 'ymd': day_list},
        {'code': '251340', 'name': 'KODEX 코스닥150선물인버스', 'ymd': day_list}
    ]

    for stock in stocks:
        simulation = Simulation(RESOURCE_PATH)
        simulation.simulate(stock)
    print ("done...")