from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, List, Optional

import pandas as pd
from cvttpy_tools.settings.cvtt_types import JsonDictT
from cvttpy_tools.tools.base import NamedObject
from cvttpy_tools.tools.logger import Log
from pt_strategy.live.ti_sender import TradingInstructionsSender
from pt_strategy.model_data_policy import ModelDataPolicy
from pt_strategy.pt_market_data import RealTimeMarketData
from pt_strategy.pt_model import Prediction
from pt_strategy.trading_pair import PairState, TradingPair

"""
  --config=pair.cfg
  --pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
"""


class TradingInstructionType(Enum):
    TARGET_POSITION = "TARGET_POSITION"

@dataclass
class TradingInstruction(NamedObject):
    type_: TradingInstructionType
    exch_instr_: ExchangeInstrument
    specifics_: Dict[str, Any]


class PtLiveStrategy(NamedObject):
    config_: Dict[str, Any]
    trading_pair_: TradingPair
    model_data_policy_: ModelDataPolicy
    pt_mkt_data_: RealTimeMarketData
    ti_sender_: TradingInstructionsSender

    # for presentation: history of prediction values and trading signals
    predictions_: pd.DataFrame
    trading_signals_: pd.DataFrame

    def __init__(
        self,
        config: Dict[str, Any],
        instruments: List[Dict[str, str]],
        ti_sender: TradingInstructionsSender,
    ):

        self.config_ = config
        self.trading_pair_ = TradingPair(config=config, instruments=instruments)
        self.predictions_ = pd.DataFrame()
        self.trading_signals_ = pd.DataFrame()
        self.ti_sender_ = ti_sender

        import copy

        # modified config must be passed to PtMarketData
        config_copy = copy.deepcopy(config)
        config_copy["instruments"] = instruments
        self.pt_mkt_data_ = RealTimeMarketData(config=config_copy)
        self.model_data_policy_ = ModelDataPolicy.create(
            config, is_real_time=True, pair=self.trading_pair_
        )
        self.open_threshold_ = self.config_.get("dis-equilibrium_open_trshld", 0.0)
        assert self.open_threshold_ > 0, "open_threshold must be greater than 0"
        self.close_threshold_ = self.config_.get("dis-equilibrium_close_trshld", 0.0)
        assert self.close_threshold_ > 0, "close_threshold must be greater than 0"

    def __repr__(self) -> str:
        return f"{self.classname()}: trading_pair={self.trading_pair_}, mdp={self.model_data_policy_.__class__.__name__}, "

    async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None:
        Log.info(f"on_mkt_data_hist_snapshot: {aggr}")
        await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr)
        pass

    async def on_mkt_data_update(self, aggr: JsonDictT) -> None:
        market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr)
        if market_data_df is not None:
            self.trading_pair_.market_data_ = market_data_df
            self.model_data_policy_.advance()
            prediction = self.trading_pair_.run(
                market_data_df, self.model_data_policy_.advance()
            )
            self.predictions_ = pd.concat(
                [self.predictions_, prediction.to_df()], ignore_index=True
            )

            trading_instructions: List[TradingInstruction] = (
                self._create_trading_instructions(
                    prediction=prediction, last_row=market_data_df.iloc[-1]
                )
            )
            if len(trading_instructions) > 0:
                await self._send_trading_instructions(trading_instructions)
            # trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
        # URGENT implement this
        pass

    async def _send_trading_instructions(
        self, trading_instructions: pd.DataFrame
    ) -> None:
        pass

    def _create_trading_instructions(
        self, prediction: Prediction, last_row: pd.Series
    ) -> List[TradingInstruction]:
        pair = self.trading_pair_
        trd_instructions: List[TradingInstruction] = []

        scaled_disequilibrium = prediction.scaled_disequilibrium_
        abs_scaled_disequilibrium = abs(scaled_disequilibrium)

        if pair.is_closed():
            if abs_scaled_disequilibrium >= self.open_threshold_:
                trd_instructions = self._create_open_trade_instructions(
                    pair, row=last_row, prediction=prediction
                )
        elif pair.is_open():
            if abs_scaled_disequilibrium <= self.close_threshold_:
                trd_instructions = self._create_close_trade_instructions(
                    pair, row=last_row, prediction=prediction
                )
            elif pair.to_stop_close_conditions(predicted_row=last_row):
                trd_instructions = self._create_close_trade_instructions(
                    pair, row=last_row
                )

        return trd_instructions

    def _create_open_trade_instructions(
        self, pair: TradingPair, row: pd.Series, prediction: Prediction
    ) -> List[TradingInstruction]:
        scaled_disequilibrium = prediction.scaled_disequilibrium_

        if scaled_disequilibrium > 0:
            side_a = "SELL"
            trd_inst_a = TradingInstruction(
                type=TradingInstructionType.TARGET_POSITION,
                exch_instr=pair.get_instrument_a(),
                specifics={"side": "SELL", "strength": -1},
            )
            side_b = "BUY"
        else:
            side_a = "BUY"
            side_b = "SELL"

        # save closing sides
        pair.user_data_["open_side_a"] = side_a  # used in oustanding positions
        pair.user_data_["open_side_b"] = side_b
        pair.user_data_["open_px_a"] = px_a
        pair.user_data_["open_px_b"] = px_b
        pair.user_data_["open_tstamp"] = tstamp

        pair.user_data_["close_side_a"] = side_b  # used for closing trades
        pair.user_data_["close_side_b"] = side_a

        # create opening trades
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_a_,
            "side": side_a,
            "action": "OPEN",
            "price": px_a,
            "disequilibrium": diseqlbrm,
            "signed_scaled_disequilibrium": scaled_disequilibrium,
            "scaled_disequilibrium": abs(scaled_disequilibrium),
            # "pair": pair,
        }
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_b_,
            "side": side_b,
            "action": "OPEN",
            "price": px_b,
            "disequilibrium": diseqlbrm,
            "scaled_disequilibrium": abs(scaled_disequilibrium),
            "signed_scaled_disequilibrium": scaled_disequilibrium,
            # "pair": pair,
        }
        return df

    def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
        trades = None
        pair = self.trading_pair_

        # Outstanding positions
        if pair.user_data_["state"] == PairState.OPEN:
            print(f"{pair}:  *** Position is NOT CLOSED. ***")
            # outstanding positions
            if self.config_["close_outstanding_positions"]:
                close_position_row = pd.Series(pair.market_data_.iloc[-2])
                # close_position_row["disequilibrium"] = 0.0
                # close_position_row["scaled_disequilibrium"] = 0.0
                # close_position_row["signed_scaled_disequilibrium"] = 0.0

                trades = self._create_close_trades(
                    pair=pair, row=close_position_row, prediction=None
                )
                if trades is not None:
                    trades["status"] = PairState.CLOSE_POSITION.name
                    print(f"CLOSE_POSITION TRADES:\n{trades}")
                    pair.user_data_["state"] = PairState.CLOSE_POSITION
                    pair.on_close_trades(trades)
            else:
                pair.add_outstanding_position(
                    symbol=pair.symbol_a_,
                    open_side=pair.user_data_["open_side_a"],
                    open_px=pair.user_data_["open_px_a"],
                    open_tstamp=pair.user_data_["open_tstamp"],
                    last_mkt_data_row=pair.market_data_.iloc[-1],
                )
                pair.add_outstanding_position(
                    symbol=pair.symbol_b_,
                    open_side=pair.user_data_["open_side_b"],
                    open_px=pair.user_data_["open_px_b"],
                    open_tstamp=pair.user_data_["open_tstamp"],
                    last_mkt_data_row=pair.market_data_.iloc[-1],
                )
        return trades

    def _trades_df(self) -> pd.DataFrame:
        types = {
            "time": "datetime64[ns]",
            "action": "string",
            "symbol": "string",
            "side": "string",
            "price": "float64",
            "disequilibrium": "float64",
            "scaled_disequilibrium": "float64",
            "signed_scaled_disequilibrium": "float64",
            # "pair": "object",
        }
        columns = list(types.keys())
        return pd.DataFrame(columns=columns).astype(types)

    def _create_open_trades(
        self, pair: TradingPair, row: pd.Series, prediction: Prediction
    ) -> Optional[pd.DataFrame]:
        colname_a, colname_b = pair.exec_prices_colnames()

        tstamp = row["tstamp"]
        diseqlbrm = prediction.disequilibrium_
        scaled_disequilibrium = prediction.scaled_disequilibrium_
        px_a = row[f"{colname_a}"]
        px_b = row[f"{colname_b}"]

        # creating the trades
        df = self._trades_df()

        print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
        if diseqlbrm > 0:
            side_a = "SELL"
            side_b = "BUY"
        else:
            side_a = "BUY"
            side_b = "SELL"

        # save closing sides
        pair.user_data_["open_side_a"] = side_a  # used in oustanding positions
        pair.user_data_["open_side_b"] = side_b
        pair.user_data_["open_px_a"] = px_a
        pair.user_data_["open_px_b"] = px_b
        pair.user_data_["open_tstamp"] = tstamp

        pair.user_data_["close_side_a"] = side_b  # used for closing trades
        pair.user_data_["close_side_b"] = side_a

        # create opening trades
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_a_,
            "side": side_a,
            "action": "OPEN",
            "price": px_a,
            "disequilibrium": diseqlbrm,
            "signed_scaled_disequilibrium": scaled_disequilibrium,
            "scaled_disequilibrium": abs(scaled_disequilibrium),
            # "pair": pair,
        }
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_b_,
            "side": side_b,
            "action": "OPEN",
            "price": px_b,
            "disequilibrium": diseqlbrm,
            "scaled_disequilibrium": abs(scaled_disequilibrium),
            "signed_scaled_disequilibrium": scaled_disequilibrium,
            # "pair": pair,
        }
        return df

    def _create_close_trades(
        self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
    ) -> Optional[pd.DataFrame]:
        colname_a, colname_b = pair.exec_prices_colnames()

        tstamp = row["tstamp"]
        if prediction is not None:
            diseqlbrm = prediction.disequilibrium_
            signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
            scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
        else:
            diseqlbrm = 0.0
            signed_scaled_disequilibrium = 0.0
            scaled_disequilibrium = 0.0
        px_a = row[f"{colname_a}"]
        px_b = row[f"{colname_b}"]

        # creating the trades
        df = self._trades_df()

        # create opening trades
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_a_,
            "side": pair.user_data_["close_side_a"],
            "action": "CLOSE",
            "price": px_a,
            "disequilibrium": diseqlbrm,
            "scaled_disequilibrium": scaled_disequilibrium,
            "signed_scaled_disequilibrium": signed_scaled_disequilibrium,
            # "pair": pair,
        }
        df.loc[len(df)] = {
            "time": tstamp,
            "symbol": pair.symbol_b_,
            "side": pair.user_data_["close_side_b"],
            "action": "CLOSE",
            "price": px_b,
            "disequilibrium": diseqlbrm,
            "scaled_disequilibrium": scaled_disequilibrium,
            "signed_scaled_disequilibrium": signed_scaled_disequilibrium,
            # "pair": pair,
        }
        del pair.user_data_["close_side_a"]
        del pair.user_data_["close_side_b"]

        del pair.user_data_["open_tstamp"]
        del pair.user_data_["open_px_a"]
        del pair.user_data_["open_px_b"]
        del pair.user_data_["open_side_a"]
        del pair.user_data_["open_side_b"]
        return df