Prepare source-only public release for develop.
Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
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"""
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Bar-based RSI scalping backtest — conservative fills, costs, no same-bar RSI lookahead.
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Mirrors RsiScalpingRobot.mqh with:
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- entries on bar open after RSI cross on prior closed bars
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- exits evaluated on prior closed bar RSI
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- trailing updated on bar close; stop checked against bar range
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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import MetaTrader5 as mt5
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import numpy as np
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import pandas as pd
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from indicator_utils import calculate_rsi
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@dataclass
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class RsiScalpParams:
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rsi_period: int = 14
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rsi_overbought: float = 71.0
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rsi_oversold: float = 57.0
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rsi_target_buy: float = 80.0
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rsi_target_sell: float = 57.0
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bars_to_wait: int = 1
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use_trailing: bool = True
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trail_distance_pts: float = 71.0
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trail_activation_pts: float = 41.0
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lot_size: float = 0.1
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@classmethod
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def from_dict(cls, d: dict[str, Any]) -> "RsiScalpParams":
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return cls(**{k: d[k] for k in cls.__dataclass_fields__ if k in d})
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@dataclass
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class CostModel:
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spread_points: float = 0.0
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slippage_points: float = 3.0
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commission_per_lot: float = 0.0
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@classmethod
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def from_symbol(cls, symbol: str, slippage_points: float = 3.0, commission_per_lot: float = 0.0) -> "CostModel":
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info = mt5.symbol_info(symbol)
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spread = float(info.spread) if info else 0.0
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return cls(spread_points=spread, slippage_points=slippage_points, commission_per_lot=commission_per_lot)
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@dataclass
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class BacktestResult:
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net_profit: float
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total_trades: int
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win_rate: float
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profit_factor: float
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max_drawdown_pct: float
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total_costs: float
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score: float
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params: RsiScalpParams
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gross_profit: float = 0.0
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gross_loss: float = 0.0
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def _calc_profit(symbol: str, order_type: int, volume: float, open_price: float, close_price: float) -> float:
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p = mt5.order_calc_profit(order_type, symbol, volume, open_price, close_price)
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return float(p) if p is not None else 0.0
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def _half_spread_price(point: float, spread_points: float) -> float:
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return (spread_points * point) / 2.0
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def _fill_buy(open_price: float, point: float, costs: CostModel, entry: bool) -> float:
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slip = costs.slippage_points * point
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hs = _half_spread_price(point, costs.spread_points)
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return open_price + hs + slip if entry else open_price - hs - slip
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def _fill_sell(open_price: float, point: float, costs: CostModel, entry: bool) -> float:
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slip = costs.slippage_points * point
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hs = _half_spread_price(point, costs.spread_points)
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return open_price - hs - slip if entry else open_price + hs + slip
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def backtest_rsi_scalping(
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df: pd.DataFrame,
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symbol: str,
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params: RsiScalpParams,
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initial_balance: float = 10_000.0,
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point: float | None = None,
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costs: CostModel | None = None,
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) -> BacktestResult:
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info = mt5.symbol_info(symbol)
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if point is None:
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point = float(info.point) if info else 0.01
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if costs is None:
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costs = CostModel.from_symbol(symbol)
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# RSI on close; decisions use index i-1 (last fully closed bar at bar i open)
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rsi_full = calculate_rsi(df["close"], params.rsi_period).to_numpy()
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times = df.index.to_numpy()
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opens = df["open"].to_numpy()
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highs = df["high"].to_numpy()
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lows = df["low"].to_numpy()
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closes = df["close"].to_numpy()
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balance = initial_balance
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peak = initial_balance
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max_dd = 0.0
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total_costs = 0.0
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position: dict[str, Any] | None = None
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rsi_against = False
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bars_against = 0
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gross_profit = 0.0
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gross_loss = 0.0
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wins = 0
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losses = 0
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trades = 0
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trail_dist = params.trail_distance_pts * point
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trail_act = (params.trail_activation_pts if params.trail_activation_pts > 0 else params.trail_distance_pts) * point
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def _update_dd() -> None:
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nonlocal peak, max_dd
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if balance > peak:
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peak = balance
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dd = (peak - balance) / peak if peak > 0 else 0.0
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if dd > max_dd:
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max_dd = dd
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def close_at(exit_mid: float) -> None:
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nonlocal balance, gross_profit, gross_loss, wins, losses, trades, position, total_costs
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if position is None:
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return
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order_type = mt5.ORDER_TYPE_BUY if position["type"] == "BUY" else mt5.ORDER_TYPE_SELL
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if position["type"] == "BUY":
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exit_price = _fill_buy(exit_mid, point, costs, entry=False)
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else:
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exit_price = _fill_sell(exit_mid, point, costs, entry=False)
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commission = costs.commission_per_lot * position["volume"] * 2.0
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profit = _calc_profit(symbol, order_type, position["volume"], position["open_price"], exit_price)
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profit -= commission
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total_costs += commission + (costs.slippage_points * point * position["volume"] * 100000 * 0.0)
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balance += profit
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trades += 1
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if profit >= 0:
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wins += 1
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gross_profit += profit
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else:
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losses += 1
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gross_loss += abs(profit)
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_update_dd()
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position = None
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def apply_trailing(bar_close: float, bar_high: float, bar_low: float) -> None:
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if position is None or not params.use_trailing or trail_dist <= 0:
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return
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if position["type"] == "BUY":
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bid = bar_close
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if bid - position["open_price"] <= trail_act:
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return
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new_sl = bid - trail_dist
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if new_sl > position.get("sl", 0.0):
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position["sl"] = new_sl
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if position.get("sl") and bar_low <= position["sl"]:
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close_at(position["sl"])
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else:
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ask = bar_close
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if position["open_price"] - ask <= trail_act:
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return
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new_sl = ask + trail_dist
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if position.get("sl", 0.0) == 0.0 or new_sl < position["sl"]:
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position["sl"] = new_sl
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if position.get("sl") and bar_high >= position["sl"]:
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close_at(position["sl"])
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start = max(params.rsi_period + 3, 3)
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for i in range(start, len(df)):
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# closed-bar RSI (no lookahead): signal bar is i-1
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rsi_sig = rsi_full[i - 1]
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rsi_prev = rsi_full[i - 2]
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rsi_two = rsi_full[i - 3]
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if np.isnan(rsi_sig) or np.isnan(rsi_prev) or np.isnan(rsi_two):
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continue
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if position is not None:
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apply_trailing(closes[i], highs[i], lows[i])
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if position is None:
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rsi_against = False
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bars_against = 0
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continue
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if position["type"] == "BUY":
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if rsi_sig < params.rsi_oversold:
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if not rsi_against:
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rsi_against = True
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bars_against = 1
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else:
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bars_against += 1
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if bars_against >= params.bars_to_wait:
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close_at(opens[i])
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else:
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if rsi_against:
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rsi_against = False
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bars_against = 0
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if rsi_sig >= params.rsi_target_buy:
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close_at(opens[i])
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else:
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if rsi_sig > params.rsi_overbought:
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if not rsi_against:
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rsi_against = True
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bars_against = 1
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else:
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bars_against += 1
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if bars_against >= params.bars_to_wait:
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close_at(opens[i])
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else:
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if rsi_against:
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rsi_against = False
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bars_against = 0
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if rsi_sig <= params.rsi_target_sell:
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close_at(opens[i])
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if position is not None:
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continue
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# entry at bar open[i] from RSI cross on bars i-2 / i-3
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if rsi_two <= params.rsi_oversold and rsi_prev > params.rsi_oversold:
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entry = _fill_buy(opens[i], point, costs, entry=True)
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position = {"type": "BUY", "volume": params.lot_size, "open_price": entry, "open_time": times[i], "sl": 0.0}
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rsi_against = False
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bars_against = 0
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elif rsi_two >= params.rsi_overbought and rsi_prev < params.rsi_overbought:
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entry = _fill_sell(opens[i], point, costs, entry=True)
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position = {"type": "SELL", "volume": params.lot_size, "open_price": entry, "open_time": times[i], "sl": 0.0}
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rsi_against = False
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bars_against = 0
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if position is not None:
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close_at(closes[-1])
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net_profit = balance - initial_balance
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win_rate = (wins / trades * 100.0) if trades else 0.0
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pf = (gross_profit / gross_loss) if gross_loss > 0 else (999.0 if gross_profit > 0 else 0.0)
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if trades < 20:
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score = net_profit - 10_000.0
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else:
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score = net_profit * (1.0 - min(max_dd, 0.5))
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return BacktestResult(
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net_profit=net_profit,
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total_trades=trades,
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win_rate=win_rate,
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profit_factor=pf,
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max_drawdown_pct=max_dd * 100.0,
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total_costs=total_costs,
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score=score,
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params=params,
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gross_profit=gross_profit,
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gross_loss=gross_loss,
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)
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def resolve_symbol(requested: str) -> str:
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key = requested.split("|")[0].strip()
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if not key:
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return requested
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if mt5.symbol_info(key) is not None:
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mt5.symbol_select(key, True)
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return key
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for suffix in (".NAS", ".NYSE", ".NYS", ".US"):
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cand = key + suffix
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if mt5.symbol_info(cand) is not None:
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mt5.symbol_select(cand, True)
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return cand
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for sym in mt5.symbols_get() or []:
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name = sym.name
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if name.startswith(key + "."):
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mt5.symbol_select(name, True)
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return name
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return key
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def load_rates(symbol: str, timeframe: int, start, end) -> pd.DataFrame:
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symbol = resolve_symbol(symbol)
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if not mt5.symbol_select(symbol, True):
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raise RuntimeError(f"Cannot select {symbol}: {mt5.last_error()}")
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rates = mt5.copy_rates_range(symbol, timeframe, start, end)
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if rates is None or len(rates) == 0:
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raise RuntimeError(f"No rates for {symbol}: {mt5.last_error()}")
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out = pd.DataFrame(rates)
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out["time"] = pd.to_datetime(out["time"], unit="s")
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out.set_index("time", inplace=True)
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return out
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def split_walk_forward(df: pd.DataFrame, train_ratio: float = 0.6) -> tuple[pd.DataFrame, pd.DataFrame]:
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cut = int(len(df) * train_ratio)
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if cut < 100 or len(df) - cut < 100:
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raise ValueError("Not enough bars for walk-forward split")
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return df.iloc[:cut].copy(), df.iloc[cut:].copy()
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