Files
trading_bot_v4/backtester/data_loader.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

39 lines
1.1 KiB
Python

"""Utilities for loading OHLCV data for local backtesting."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
import pandas as pd
@dataclass
class DataSlice:
symbol: str
timeframe: str
data: pd.DataFrame
def load_csv(path: Path, symbol: str, timeframe: str, start: Optional[str] = None, end: Optional[str] = None) -> DataSlice:
if not path.exists():
raise FileNotFoundError(f"Missing data file: {path}")
df = pd.read_csv(path, parse_dates=["timestamp"])
df = df.sort_values("timestamp").reset_index(drop=True)
if start:
df = df[df["timestamp"] >= pd.Timestamp(start)]
if end:
df = df[df["timestamp"] <= pd.Timestamp(end)]
if df.empty:
raise ValueError("No rows remain after applying date filters")
expected_cols = {"timestamp", "open", "high", "low", "close", "volume"}
missing = expected_cols.difference(df.columns)
if missing:
raise ValueError(f"Missing columns in {path}: {sorted(missing)}")
df = df.set_index("timestamp")
return DataSlice(symbol=symbol.upper(), timeframe=timeframe, data=df)