# https://bibot.tistory.com/63
# https://nonmeyet.tistory.com/entry/Python-TALib%EB%A5%BC-%ED%99%9C%EC%9A%A9%ED%95%9C-%EB%B9%84%ED%8A%B8%EC%BD%94%EC%9D%B8%EC%A3%BC%EA%B0%80%EA%B8%B0%EC%88%A0%EB%B6%84%EC%84%9D-%EB%B3%B4%EC%A1%B0%EC%A7%80%ED%91%9C-%EC%B6%94%EA%B0%80
# https://lunadaddy.tistory.com/122
# https://wikidocs.net/186885

import os
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
import pyupbit
import math
import sqlite3
# https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
# https://lunadaddy.tistory.com/122
import talib
import pandas as pd
from datetime import datetime, timedelta

from stock.analysis.IchimokuCloud import IchimokuCloud

class JSDPattern:
    RESOURCE_PATH = None
    ichimokuCloud = None
    scaler = None

    def __init__(self, RESOURCE_PATH=None):
        if RESOURCE_PATH is None:
            self.RESOURCE_PATH = os.path.join(os.getcwd(), "resources")
        else:
            self.RESOURCE_PATH = RESOURCE_PATH

        self.ichimokuCloud = IchimokuCloud()
        return

    def makeTickData(self, data, mins=1):
        result = {
            "ymd": [],
            "open": [], "close": [], "high": [], "low": [], "volume": [], "volume_up": [], "volume_down": [], "volume_updown_diff": []
        }

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

            if data['open'][i-1] < data['close'][i-1]:
                result["volume_up"].append(data['volume'][i-1])
                result["volume_down"].append(0)
            elif data['close'][i-1] < data['open'][i-1]:
                result["volume_down"].append(-1*data['volume'][i-1])
                result["volume_up"].append(0)
            else:
                result["volume_up"].append(0)
                result["volume_down"].append(0)

            up = [data['volume'][i - mins + c] for c in range(len(data['volume'][i - mins: i])) if data['close'][i - mins + c] < data['open'][i - mins + c]]
            down = [data['volume'][i - mins + c] for c in range(len(data['volume'][i - mins: i])) if data['close'][i - mins + c] < data['open'][i - mins + c]]
            result["volume_updown_diff"].append(sum(up) - sum(down))

        return result

    def append(self, df=None, result=None):
        data = {
            "ymd": [],
            "open": [], "close": [], "high": [], "low": [], "volume": []
        }

        if result is not None:
            for i in range(len(result['ymd'])):
                data['ymd'].append(result['ymd'][i])
                data['open'].append(result['open'][i])
                data['close'].append(result['close'][i])
                data['high'].append(result['high'][i])
                data['low'].append(result['low'][i])
                data['volume'].append(result['volume'][i])

        if df is not None:
            for i in range(len(df)):
                data['ymd'].append(df.index[i])
                data['open'].append(df['open'].iloc[i])
                data['close'].append(df['close'].iloc[i])
                data['high'].append(df['high'].iloc[i])
                data['low'].append(df['low'].iloc[i])
                data['volume'].append(df['volume'].iloc[i])

        return data

    def getDBData(self, stock_code, day, mins, get_days=14):

        if mins == 3:
            table = 'minute3'
        elif mins == 5:
            table = 'minute5'
        elif mins == 10:
            table = 'minute10'
        elif mins == 20:
            table = 'minute20'
        elif mins == 30:
            table = 'minute30'
        elif mins == 60:
            table = 'minute60'
        elif mins == 200:
            table = 'minute200'
        elif mins == 1440:
            table = 'daily'
        else:
            table = 'minutely'

        conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, 'coins.db'))
        cursor = conn.cursor()

        result = {"ymd": [], "open": [], "close": [], "high": [], "low": [], "volume": [], "label": []}
        for i in range(get_days, -1, -1):
            this_day = (datetime.strptime(day, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
            cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + table + ' WHERE (CODE=? or CODE=?) and (ymd=?) order by ymd, hms', (stock_code, stock_code.replace('KRW-', ''), this_day,))

            db_result = cursor.fetchall()
            for rows in db_result:
                ymd = rows[0]  # hts.날짜
                hms = rows[1]  # hts.시간
                open = rows[2]  # hts.시가
                high = rows[3]  # hts.고가
                low = rows[4]  # hts.저가
                close = rows[5]  # hts.종가
                vol = rows[6]  # hts.거래량

                temp = datetime.strptime(str(ymd) + " " + hms, '%Y%m%d %H%M%S')

                result["ymd"].append(temp)
                result["open"].append(float(open))
                result["close"].append(float(close))
                result["high"].append(float(high))
                result["low"].append(float(low))
                result["volume"].append(float(vol))

        cursor.close()
        conn.close()

        return result

    def getCoinData(self, ticker, mins=None, to=None, ymd=None, get_days=14):
        result = None

        if ymd is not None and datetime.now() < datetime.strptime(ymd, '%Y%m%d'):
            ymd = None

        if ymd is None:
            if to is None:
                if mins is None:
                    df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'])
                else:
                    if mins == 1440:
                        df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute1', count=1)
                    else:
                        df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute' + str(mins))
            else:
                df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute' + str(mins), to=to)

            if df is not None:
                df["datetime"] = df.index
                df = df[['open', 'high', 'low', 'close', 'volume']].astype(float)

            if mins is not None:
                result = self.getDBData(ticker['ticker_code'], datetime.today().strftime('%Y%m%d'), mins=mins, get_days=get_days)

            data = self.append(df, result)

        else:
            result = self.getDBData(ticker['ticker_code'], ymd, mins=mins, get_days=get_days)
            data = self.append(df=None, result=result)

        return data

    def is_Support(self, low, i, observation_time=5):
        # https://sine-qua-none.tistory.com/198

        # c1 = df.Low[i] < df.Low[i - 1] < df.Low[i - 2] < df.Low[i - 3]
        # c2 = df.Low[i] < df.Low[i + 1] < df.Low[i + 2] < df.Low[i + 3]
        # return c1 & c2

        #if low[i] == np.min(low[i - 2*self.observation_time:i + 1]):
        if low[i] == np.min(low[i - observation_time:i + observation_time + 1]):
            return True
        else:
            return False

    def is_Resistance(self, high, i, observation_time=5):
        # https://sine-qua-none.tistory.com/198

        # c1 = df.High[i] > df.High[i - 1] > df.High[i - 2] > df.High[i - 3]
        # c2 = df.High[i] > df.High[i + 1] > df.High[i + 2] > df.High[i + 3]
        # return c1 & c2
        # if df['high'][i] == np.max(df['high'][i - self.observation_time:i + self.observation_time + 1]):
        #if high[i] == np.max(high[i - 2*self.observation_time:i + 1]):
        if high[i] == np.max(high[i - observation_time:i + observation_time + 1]):
            return True
        else:
            return False


    def getDiff_Rate(self, price1, price2, duration=1440, move=None):
        # price1: close, price2: laggingSpan_27
        diff = [0 for i in range(len(price1))]
        diff_rate = [0 for i in range(len(price1))]

        for i in range(0, len(price1)):
            if price1[i] is not None and not math.isnan(price1[i]) and price2[i] is not None and not math.isnan(price2[i]):
                diff[i] = price1[i] - price2[i]
            else:
                diff[i] = np.nan

        if len(price1) < duration:
            duration = 52

        for i in range(0, len(price1)):

            if duration <= i:
                l = [d for d in diff[i - duration:i + 1] if d is not None and 0 < d]
                if 0 < len(l):
                    min_v_p = np.min(l)
                else:
                    min_v_p = 0
                l = [d for d in diff[i - duration:i + 1] if d is not None and 0 < d]
                if 0 < len(l):
                    max_v_p = np.max(l)
                else:
                    max_v_p = 0
                l = [d for d in diff[i - duration:i + 1] if d is not None and d < 0]
                if 0 < len(l):
                    min_v_m = np.min(l)
                else:
                    min_v_m = 0
                l = [d for d in diff[i - duration:i + 1] if d is not None and d < 0]
                if 0 < len(l):
                    max_v_m = np.max(l)
                else:
                    max_v_m = 0

                if diff[i] is not None and not math.isnan(diff[i]):
                    if 0 <= diff[i]:
                        if max_v_p - min_v_p == 0:
                            diff_rate[i] = 0
                        else:
                            diff_rate[i] = (diff[i] - min_v_p) / (max_v_p - min_v_p)
                    else:
                        if max_v_m - min_v_m == 0:
                            diff_rate[i] = 0
                        else:
                            diff_rate[i] = ((diff[i] - min_v_m) / (max_v_m - min_v_m)) - 1
                else:
                    diff_rate[i] = np.nan

        return diff, diff_rate


    def getDisparity_low_min(self, ticker, min='minutely'):
        try:
            self.conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "coins.db"))
            self.cursor = self.conn.cursor()

            self.cursor.execute("SELECT lowest_disparity FROM disparity WHERE (CODE=? or CODE=?) AND TYPE=?", (ticker['ticker_code'], ticker['ticker_code'].replace('KRW-', ''), min))
            db_result = self.cursor.fetchall()
            self.cursor.close()
            self.conn.close()

            if 0 < len(db_result):
                return db_result[0][0]

        except:
            return 0.90
        return 0.90