Auto202605030340

import json
import logging
import sys
from datetime import datetime, timedelta
from pathlib import Path

from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy
from pandas import DataFrame

# Resolve project root from this file's location (works on any machine)
_BASE = Path(__file__).resolve().parent.parent.parent
if str(_BASE) not in sys.path:
    sys.path.insert(0, str(_BASE))

from risk.risk_manager import run_all_checks
from sentiment.reader import get_sentiment_signal

logger = logging.getLogger(__name__)


class Auto202605030340(IStrategy):
    """
    Hypothesis: Buy BTC when RSI below 30.
    Integrates Cipher Sentiment Gate and Risk Manager.
    """

    # Strategy Interface Version
    INTERFACE_VERSION = 3

    # Timeframe and candle settings
    timeframe = "5m"
    startup_candle_count: int = 50

    # Risk Management Settings
    stoploss = -0.04
    stoploss_on_exchange = True

    # Minimal ROI (Empty as we primarily use stoploss or custom exit)
    minimal_roi = {
        "0": 0.1,  # Exit at 10% profit
        "60": 0.05,
        "120": 0.02,
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Calculate indicators using polars.
        """
        from qnt.polars_indicators import add_rsi
        from qnt.polars_ohlcv import ohlcv_to_pandas, pandas_to_polars

        df_pl = pandas_to_polars(dataframe)

        # RSI calculation (Standard 14 period)
        df_pl = add_rsi(df_pl, period=14, alias="rsi")

        dataframe = ohlcv_to_pandas(df_pl)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define entry conditions: RSI < 30.
        """
        dataframe.loc[((dataframe["rsi"] < 30) & (dataframe["volume"] > 0)), "enter_long"] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define exit conditions: RSI > 70 (Standard counter-signal).
        """
        dataframe.loc[(dataframe["rsi"] > 70), "exit_long"] = 1

        return dataframe

    def confirm_trade_entry(
        self,
        pair: str,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        current_time: datetime,
        entry_tag: str,
        side: str,
        **kwargs,
    ) -> bool:
        """
        Custom confirmation layer integrating Sentiment and Risk checks.
        """

        # --- LAYER 1: RISK CHECKS ---
        try:
            # 1. Gather balance info
            total_balance = self.wallets.get_total_stake_amount()

            # 2. Fetch recent trades as list of dicts (Requirement)
            # Trade.get_trades_proxy returns trade objects
            all_recent_trades = Trade.get_trades_proxy(is_open=False)
            recent_trades_data = [
                {"profit_ratio": t.close_profit, "close_date": t.close_date}
                for t in all_recent_trades
            ][:10]  # Take last 10 for analysis

            # 3. Count trades in the last hour
            one_hour_ago = current_time - timedelta(hours=1)
            trades_last_hour = len(
                [t for t in all_recent_trades if t.close_date and t.close_date >= one_hour_ago]
            )

            # 4. Load balance baselines from state file
            state_file = _BASE / "risk/balance_state.json"
            if state_file.exists():
                with open(state_file) as f:
                    state = json.load(f)
                start_of_day = state.get("start_of_day", total_balance)
                start_of_week = state.get("start_of_week", total_balance)
            else:
                start_of_day = total_balance
                start_of_week = total_balance

            # 5. Execute all Risk Manager checks
            risk_result = run_all_checks(
                current_balance=total_balance,
                start_of_day_balance=start_of_day,
                start_of_week_balance=start_of_week,
                trade_amount_usdt=amount * rate,
                trades_last_hour=trades_last_hour,
                recent_trades=recent_trades_data,
            )

            if not risk_result["safe_to_trade"]:
                logger.warning(
                    f"[RISK BLOCK] {pair} blocked. Reasons: {risk_result['blocking_reasons']}"
                )
                return False

        except Exception as e:
            logger.error(f"[RISK ERROR] Failed to perform risk checks: {e}")
            # In case of system error, we fail-safe by blocking entry
            return False

        # --- LAYER 2: SENTIMENT CHECK ---
        sentiment_signal = get_sentiment_signal()

        if sentiment_signal == "BEARISH":
            logger.info(f"[SENTIMENT BLOCK] {pair} entry blocked due to BEARISH market sentiment.")
            return False

        # If both layers pass
        logger.info(f"[ENTRY ALLOWED] {pair} passed all risk and sentiment gates.")
        return True