import pandas as pd
import numpy as np
from .indicator import Indicator
from .config import QUERY_CONDITION
from logic.nadaraya import NadarayaWatson


class Strategy:
    """
        Signal: -1 is null, 0 is buy and 1 is sell
    """
    def __init__(self) -> None:
        pass

    @staticmethod
    def rsi_strategy(rsi_df, threshold=(20, 80)) -> pd.DataFrame:
        low, high = threshold[0], threshold[1]
        signal_values = []
        loop_df = rsi_df[["stoch_rsi",
                          "stoch_rsi_smooth_k",
                          "stoch_rsi_smooth_d"]]
        for stoch, smooth_k, smooth_d in loop_df.values:
            if stoch <= low and \
                smooth_k <= low and \
                    smooth_d <= low:
                signal_values.append(0)
            elif stoch >= high and \
                smooth_k >= high and \
                    smooth_d >= high:
                signal_values.append(1)
            else:
                signal_values.append(-1)
        rsi_df["rsi_signal"] = signal_values
        return rsi_df

    @staticmethod
    def ichimoku_strategy(df) -> pd.DataFrame:
        signal_values_1, signal_values_3 = [-1], [-1]
        for index in range(1, df.shape[0]):
            prev = df.iloc[index - 1]
            signal_1, signal_3 = -1, -1
            if (not (np.isnan(df["tenkan_kijun"].iloc[index])) and
                    df["tenkan_kijun"].iloc[index - 1: index + 1].prod() < 0):
                if prev["tenkan_kijun"] < 0 and prev["tenkan_sen"] < min(
                    prev["senkou_span_a"], prev["senkou_span_b"]
                ):
                    signal_1 = 0
                elif prev["tenkan_kijun"] > 0 and prev["tenkan_sen"] > max(
                    prev["senkou_span_a"], prev["senkou_span_b"]
                ):
                    signal_1 = 1
            signal_values_1.append(signal_1)
            if (not (np.isnan(df["kumo_cloud"].iloc[index])) and
                    df["kumo_cloud"].iloc[index - 1: index + 1].prod() < 0):
                if prev["kumo_cloud"] < 0:
                    signal_3 = 0
                elif prev["kumo_cloud"] > 0:
                    signal_3 = 1
            signal_values_3.append(signal_3)
        df["ichimoku_signal_1"] = signal_values_1
        df["ichimoku_signal_3"] = signal_values_3
        return df

    @staticmethod
    def macd_strategy(macd_df) -> pd.DataFrame:
        signal_values = []
        for macd in macd_df["macd"]:
            if macd < 0:
                signal_values.append(0)
            elif macd > 0:
                signal_values.append(1)
            else:
                signal_values.append(-1)
        macd_df["macd_signal"] = signal_values
        return macd_df

    @staticmethod
    def get_daily_report(price_records,
                         rsi_df,
                         macd_df,
                         last_date,
                         lst_symbols):
        try:
            result = []
            for index, symbol in enumerate(lst_symbols):
                symbol_rsi_df = rsi_df[["time", symbol]]
                symbol_macd_df = macd_df[["time", symbol]]
                price_values = []
                for price_record in price_records:
                    tmp_record = price_record["value"][index]
                    tmp_record["time"] = price_record["time"]
                    price_values.append(tmp_record)
                symbol_price_df = pd.DataFrame(price_values)
                symbol_rsi_df = \
                    symbol_rsi_df[["time", symbol]].rename(
                        columns={symbol: "rsi"})
                symbol_rsi_df = Indicator.get_all_rsi(symbol_rsi_df)
                symbol_rsi_df = Strategy.rsi_strategy(symbol_rsi_df)
                symbol_rsi_df = symbol_rsi_df.iloc[-1:]

                symbol_macd_df = \
                    symbol_macd_df[["time", symbol]].rename(
                        columns={symbol: "macd"})
                symbol_macd_df = Strategy.macd_strategy(symbol_macd_df)
                symbol_macd_df = symbol_macd_df.iloc[-1:]

                df = pd.merge(symbol_rsi_df,
                              symbol_macd_df,
                              how="inner",
                              on="time")
                symbol_ichi_df = Indicator.get_ichimoku_cloud(symbol_price_df)
                symbol_ichi_df = Strategy.ichimoku_strategy(symbol_ichi_df)
                symbol_ichi_df = symbol_ichi_df[["time",
                                                 "ichimoku_signal_1",
                                                 "ichimoku_signal_3"]]
                symbol_ichi_df["ticker"] = symbol
                df = pd.merge(df, symbol_ichi_df, how="right", on="time")
                query_df = df[df["time"] >= last_date]
                query_df = query_df.query(QUERY_CONDITION)
                query_df = query_df.fillna(-1)
                result.extend(query_df.to_dict(orient="records"))
            result_df = pd.DataFrame(result)
            return result_df
        except Exception as e:
            return {"message": f"Caught error {e}"}

    @staticmethod
    def nadaraya_strategy(symbol, price, envelope) -> dict:
        open_signal, close_signal = -1, -1
        if price["open"] >= envelope[0]:
            open_signal = 1
        elif price["open"] <= envelope[1]:
            open_signal = 0
        if price["close"] >= envelope[0]:
            close_signal = 1
        elif price["close"] <= envelope[1]:
            close_signal = 0
        return [{"ticker": symbol,
                 "open_signal": open_signal,
                 "close_signal": close_signal}]

    @staticmethod
    def get_daily_nadaraya(price_records,
                           lst_symbols,
                           last_date):
        try:
            result = []
            for index, symbol in enumerate(lst_symbols):
                price_values = []
                for price_record in price_records:
                    tmp_record = price_record["value"][index]
                    tmp_record["time"] = price_record["time"]
                    price_values.append(tmp_record)
                symbol_price_df = pd.DataFrame(price_values).iloc[-201:]
                nada_envelopes = \
                    NadarayaWatson.nadaraya_watson_envelope_indicator(
                        symbol_price_df["close"])
                current_price = symbol_price_df.iloc[-1]
                signal = Strategy.nadaraya_strategy(symbol,
                                                    current_price,
                                                    nada_envelopes[-1])
                result.extend(signal)
            result_df = pd.DataFrame(result)
            result_df = result_df.query("open_signal > -1 | close_signal > -1")
            return result_df
        except Exception as e:
            return {"message": f"Caught error {e}"}