Files
trading_bot_v4/backtester/simulator.py
mindesbunister 5f7702469e remove: V10 momentum system - backtest proved it adds no value
- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript)
- Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties)
- Removed backtest result files (sweep_*.csv)
- Updated copilot-instructions.md to remove v10 references
- Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+)

Rationale:
- 1,944 parameter combinations tested in backtest
- All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR)
- Momentum parameters had ZERO impact on trade selection
- Profit factor 1.027 too low (barely profitable after fees)
- Max drawdown -$1,270 vs +$498 profit = terrible risk-reward
- v10 penalties were blocking good trades (bug: applied to wrong positions)

Keeping v9 as production system - simpler, proven, effective.
2025-11-28 22:35:32 +01:00

388 lines
12 KiB
Python

from __future__ import annotations
from dataclasses import dataclass, field
from typing import Callable, List, Optional
import numpy as np
import pandas as pd
from backtester.indicators.money_line import (
Direction,
MoneyLineInputs,
MoneyLineSignal,
money_line_signals,
)
QualityFilter = Callable[[MoneyLineSignal], bool]
@dataclass
class TradeConfig:
position_size: float = 1000.0
take_profit_1_size_percent: float = 60.0
atr_multiplier_tp1: float = 2.0
atr_multiplier_tp2: float = 4.0
atr_multiplier_sl: float = 3.0
min_tp1_percent: float = 0.5
max_tp1_percent: float = 1.5
min_tp2_percent: float = 1.0
max_tp2_percent: float = 3.0
min_sl_percent: float = 0.8
max_sl_percent: float = 2.0
fallback_tp1_percent: float = 0.8
fallback_tp2_percent: float = 1.7
fallback_sl_percent: float = 1.3
trailing_atr_multiplier: float = 1.5
trailing_min_percent: float = 0.25
trailing_max_percent: float = 0.9
max_bars_per_trade: Optional[int] = None
@dataclass
class SimulatedTrade:
symbol: str
direction: Direction
signal_type: str
entry_time: pd.Timestamp
exit_time: pd.Timestamp
entry_price: float
exit_price: float
realized_pnl: float
profit_percent: float
exit_reason: str
bars_held: int
tp1_hit: bool
tp2_hit: bool
trailing_active: bool
mae_percent: float
mfe_percent: float
quality_score: Optional[float] = None
adx_at_entry: Optional[float] = None
atr_at_entry: Optional[float] = None
runner_size: float = 0.0
tp1_size: float = 0.0
_exit_index: int = field(repr=False, default=-1)
@dataclass
class SimulationResult:
trades: List[SimulatedTrade]
@property
def total_pnl(self) -> float:
return sum(t.realized_pnl for t in self.trades)
@property
def win_rate(self) -> float:
wins = sum(1 for t in self.trades if t.realized_pnl > 0)
return 0.0 if not self.trades else wins / len(self.trades)
@property
def average_pnl(self) -> float:
return 0.0 if not self.trades else self.total_pnl / len(self.trades)
@property
def max_drawdown(self) -> float:
equity = 0.0
peak = 0.0
max_dd = 0.0
for trade in self.trades:
equity += trade.realized_pnl
peak = max(peak, equity)
max_dd = min(max_dd, equity - peak)
return max_dd
def simulate_money_line(
df: pd.DataFrame,
symbol: str,
inputs: Optional[MoneyLineInputs] = None,
config: Optional[TradeConfig] = None,
quality_filter: Optional[QualityFilter] = None,
) -> SimulationResult:
if inputs is None:
inputs = MoneyLineInputs()
if config is None:
config = TradeConfig()
if quality_filter is None:
quality_filter = lambda _: True # type: ignore
data = df.sort_index().copy()
index_positions = {ts: idx for idx, ts in enumerate(data.index)}
signals = money_line_signals(data, inputs)
trades: List[SimulatedTrade] = []
next_available_index = 0
for signal in signals:
if signal.timestamp not in index_positions:
continue
start_idx = index_positions[signal.timestamp]
if start_idx < next_available_index:
continue
if not quality_filter(signal):
continue
trade = _simulate_trade(data, start_idx, signal, symbol, config)
if trade is None:
continue
trades.append(trade)
next_available_index = trade._exit_index
return SimulationResult(trades=trades)
def _simulate_trade(
data: pd.DataFrame,
start_idx: int,
signal: MoneyLineSignal,
symbol: str,
config: TradeConfig,
) -> Optional[SimulatedTrade]:
if start_idx >= len(data) - 1:
return None
entry_price = float(signal.entry_price)
if not np.isfinite(entry_price) or entry_price <= 0:
return None
tp1_percent = _percent_from_atr(
signal.atr,
entry_price,
config.atr_multiplier_tp1,
config.min_tp1_percent,
config.max_tp1_percent,
config.fallback_tp1_percent,
)
tp2_percent = _percent_from_atr(
signal.atr,
entry_price,
config.atr_multiplier_tp2,
config.min_tp2_percent,
config.max_tp2_percent,
config.fallback_tp2_percent,
)
sl_percent = _percent_from_atr(
signal.atr,
entry_price,
config.atr_multiplier_sl,
config.min_sl_percent,
config.max_sl_percent,
config.fallback_sl_percent,
)
direction = signal.direction
tp1_price = _target_price(entry_price, tp1_percent, direction)
tp2_price = _target_price(entry_price, tp2_percent, direction)
stop_price = _stop_price(entry_price, sl_percent, direction)
tp1_fraction = config.take_profit_1_size_percent / 100.0
tp1_fraction = np.clip(tp1_fraction, 0.0, 1.0)
tp1_size = config.position_size * tp1_fraction
runner_size = config.position_size - tp1_size
tp1_hit = False
tp2_hit = False
trailing_active = False
remaining_size = config.position_size
realized_pnl = 0.0
exit_reason = "TIME"
exit_price = entry_price
exit_idx = start_idx
bars_held = 0
mae = 0.0
mfe = 0.0
runner_stop_percent = _runner_stop_offset(signal.adx)
runner_stop_price = _stop_price(entry_price, runner_stop_percent, direction)
trailing_stop_price = runner_stop_price
favorable_price = entry_price
max_bars = config.max_bars_per_trade or len(data)
for idx in range(start_idx + 1, len(data)):
bar = data.iloc[idx]
bar_high = float(bar.high)
bar_low = float(bar.low)
bars_held += 1
mae = min(mae, _profit_percent(bar_low if direction == "long" else bar_high, entry_price, direction))
mfe = max(mfe, _profit_percent(bar_high if direction == "long" else bar_low, entry_price, direction))
if not tp1_hit:
if _stop_hit(bar_low, bar_high, stop_price, direction):
realized_pnl += config.position_size * _profit_percent(stop_price, entry_price, direction) / 100.0
exit_reason = "SL"
exit_price = stop_price
exit_idx = idx
break
if _target_hit(bar_low, bar_high, tp1_price, direction):
tp1_hit = True
if tp1_size > 0:
realized_pnl += tp1_size * _profit_percent(tp1_price, entry_price, direction) / 100.0
remaining_size -= tp1_size
exit_reason = "TP1"
runner_stop_price = _stop_price(entry_price, runner_stop_percent, direction)
trailing_stop_price = runner_stop_price
favorable_price = entry_price
# Continue evaluating same bar for runner logic
else:
if remaining_size <= 0:
exit_reason = "TP1"
exit_price = tp1_price
exit_idx = idx
break
if _stop_hit(bar_low, bar_high, runner_stop_price, direction):
realized_pnl += remaining_size * _profit_percent(runner_stop_price, entry_price, direction) / 100.0
exit_reason = "BREAKEVEN" if runner_stop_percent == 0 else "RUNNER_SL"
exit_price = runner_stop_price
exit_idx = idx
break
if (not tp2_hit) and _target_hit(bar_low, bar_high, tp2_price, direction):
tp2_hit = True
trailing_active = True
exit_reason = "TP2"
favorable_price = _update_favorable_price(favorable_price, bar_high, bar_low, direction)
trailing_stop_price = _compute_trailing_stop(
favorable_price,
entry_price,
signal.atr,
config,
direction,
)
if trailing_active:
favorable_price = _update_favorable_price(favorable_price, bar_high, bar_low, direction)
trailing_stop_price = _compute_trailing_stop(
favorable_price,
entry_price,
signal.atr,
config,
direction,
)
if _stop_hit(bar_low, bar_high, trailing_stop_price, direction):
realized_pnl += remaining_size * _profit_percent(trailing_stop_price, entry_price, direction) / 100.0
exit_reason = "TRAILING_SL"
exit_price = trailing_stop_price
exit_idx = idx
break
if bars_held >= max_bars:
exit_price = float(bar.close)
realized_pnl += remaining_size * _profit_percent(exit_price, entry_price, direction) / 100.0
exit_reason = "MAX_TIME"
exit_idx = idx
break
else:
final_bar = data.iloc[-1]
exit_price = float(final_bar.close)
realized_pnl += remaining_size * _profit_percent(exit_price, entry_price, direction) / 100.0
exit_reason = "END"
exit_idx = len(data) - 1
exit_time = data.index[exit_idx]
profit_percent = realized_pnl / config.position_size * 100 if config.position_size else 0.0
return SimulatedTrade(
symbol=symbol,
direction=direction,
signal_type=signal.signal_type,
entry_time=signal.timestamp,
exit_time=exit_time,
entry_price=entry_price,
exit_price=exit_price,
realized_pnl=realized_pnl,
profit_percent=profit_percent,
exit_reason=exit_reason,
bars_held=bars_held,
tp1_hit=tp1_hit,
tp2_hit=tp2_hit,
trailing_active=trailing_active,
mae_percent=mae,
mfe_percent=mfe,
quality_score=None,
adx_at_entry=signal.adx,
atr_at_entry=signal.atr,
runner_size=runner_size,
tp1_size=tp1_size,
_exit_index=exit_idx,
)
def _percent_from_atr(
atr_value: float,
price: float,
multiplier: float,
min_percent: float,
max_percent: float,
fallback: float,
) -> float:
if price <= 0:
return fallback
atr_percent = (atr_value / price) * 100 if price else 0.0
if atr_percent == 0:
return fallback
percent = atr_percent * multiplier
return float(np.clip(percent, min_percent, max_percent))
def _target_price(entry: float, percent: float, direction: Direction) -> float:
if direction == "long":
return entry * (1 + percent / 100.0)
return entry * (1 - percent / 100.0)
def _stop_price(entry: float, percent: float, direction: Direction) -> float:
if direction == "long":
return entry * (1 - percent / 100.0)
return entry * (1 + percent / 100.0)
def _stop_hit(bar_low: float, bar_high: float, stop_price: float, direction: Direction) -> bool:
if direction == "long":
return bar_low <= stop_price
return bar_high >= stop_price
def _target_hit(bar_low: float, bar_high: float, target_price: float, direction: Direction) -> bool:
if direction == "long":
return bar_high >= target_price
return bar_low <= target_price
def _profit_percent(price: float, entry: float, direction: Direction) -> float:
if entry == 0:
return 0.0
if direction == "long":
return (price - entry) / entry * 100.0
return (entry - price) / entry * 100.0
def _runner_stop_offset(adx: float) -> float:
if adx < 20:
return 0.0
if adx < 25:
return 0.3
return 0.55
def _update_favorable_price(current: float, bar_high: float, bar_low: float, direction: Direction) -> float:
if direction == "long":
return max(current, bar_high)
return min(current, bar_low)
def _compute_trailing_stop(
favorable_price: float,
entry_price: float,
atr_value: float,
config: TradeConfig,
direction: Direction,
) -> float:
atr_percent = (atr_value / entry_price) * 100 if entry_price else 0.0
trail_percent = atr_percent * config.trailing_atr_multiplier
trail_percent = float(np.clip(trail_percent, config.trailing_min_percent, config.trailing_max_percent))
if direction == "long":
return favorable_price * (1 - trail_percent / 100.0)
return favorable_price * (1 + trail_percent / 100.0)