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ML-3m-trader Backtesting Engine
=================================
Walk-forward backtester with realistic execution modeling:
- Per-trade position sizing by balance and SL distance
- Random slippage (0-2 XAUUSDc units)
- Spread filter (skip if SL < spread * 10)
- 1:1 Risk-Reward enforcement
"""
import numpy as np
import pandas as pd
import config as cfg
def run_backtest(
df: pd.DataFrame,
predictions: np.ndarray,
starting_balance: float = cfg.STARTING_BALANCE,
bet_pct: float = cfg.DEFAULT_BET_PCT,
seed: int = cfg.RANDOM_SEED,
) -> dict:
"""
Walk-forward backtest on the test set.
Parameters
----------
df : pd.DataFrame
Test-set DataFrame with OHLCV + spread + features.
predictions : np.ndarray
Model predictions aligned with df (BUY=1, SELL=2, HOLD=3, DO_NOTHING=0).
starting_balance : float
Initial account balance in USD.
bet_pct : float
Fraction of balance to risk per trade.
seed : int
RNG seed for slippage.
Returns
-------
dict with keys:
trades : list of dicts (individual trade records)
equity_curve : np.ndarray (balance after each bar)
final_balance: float
"""
rng = np.random.RandomState(seed)
high = df["high"].values.astype(np.float64)
low = df["low"].values.astype(np.float64)
close = df["close"].values.astype(np.float64)
# Spread handling (same heuristic as labeler)
spread_raw = df["spread"].values.astype(np.float64)
point = 0.01
if np.nanmedian(spread_raw) < 1.0:
spread = spread_raw * point
else:
spread = spread_raw
# Pre-compute ATR for SL distance
atr = _compute_atr_vec(high, low, close, cfg.ATR_PERIOD)
n = len(close)
balance = starting_balance
equity_curve = np.full(n, starting_balance, dtype=np.float64)
trades = []
in_trade = False
trade_dir = 0 # 1=long, -1=short
entry_price = 0.0
sl_price = 0.0
tp_price = 0.0
trade_entry_bar = 0
risk_amount = 0.0
for i in range(n):
if in_trade:
# Check exit
hit_sl = False
hit_tp = False
if trade_dir == 1: # Long
if low[i] <= sl_price:
hit_sl = True
if high[i] >= tp_price:
hit_tp = True
else: # Short
if high[i] >= sl_price:
hit_sl = True
if low[i] <= tp_price:
hit_tp = True
if hit_tp and hit_sl:
# Ambiguous bar — assume SL hit (conservative)
hit_tp = False
if hit_sl:
pnl = -risk_amount
balance += pnl
trades.append({
"entry_bar": trade_entry_bar,
"exit_bar": i,
"direction": "BUY" if trade_dir == 1 else "SELL",
"entry_price": entry_price,
"sl_price": sl_price,
"tp_price": tp_price,
"exit_price": sl_price,
"pnl": pnl,
"result": "SL",
"balance_after": balance,
"time_entry": df["time"].iloc[trade_entry_bar] if "time" in df.columns else None,
"time_exit": df["time"].iloc[i] if "time" in df.columns else None,
})
in_trade = False
elif hit_tp:
pnl = risk_amount # 1:1 RR
balance += pnl
trades.append({
"entry_bar": trade_entry_bar,
"exit_bar": i,
"direction": "BUY" if trade_dir == 1 else "SELL",
"entry_price": entry_price,
"sl_price": sl_price,
"tp_price": tp_price,
"exit_price": tp_price,
"pnl": pnl,
"result": "TP",
"balance_after": balance,
"time_entry": df["time"].iloc[trade_entry_bar] if "time" in df.columns else None,
"time_exit": df["time"].iloc[i] if "time" in df.columns else None,
})
in_trade = False
# Try to open new trade if not in one
if not in_trade and predictions[i] in (cfg.LABEL_BUY, cfg.LABEL_SELL):
if np.isnan(atr[i]) or atr[i] <= 0:
equity_curve[i] = balance
continue
sl_dist = atr[i] * cfg.ATR_SL_MULTIPLIER
# Spread filter
if sl_dist < spread[i] * cfg.SPREAD_FILTER_MULTIPLIER:
equity_curve[i] = balance
continue
# Random slippage
slippage = rng.uniform(cfg.SLIPPAGE_MIN, cfg.SLIPPAGE_MAX)
if predictions[i] == cfg.LABEL_BUY:
entry_price = close[i] + slippage # buy at worse price
sl_price = entry_price - sl_dist
tp_price = entry_price + sl_dist
trade_dir = 1
else: # SELL
entry_price = close[i] - slippage # sell at worse price
sl_price = entry_price + sl_dist
tp_price = entry_price - sl_dist
trade_dir = -1
# Position sizing: risk bet_pct of balance
risk_amount = balance * bet_pct
trade_entry_bar = i
in_trade = True
equity_curve[i] = balance
# Close any open trade at last bar's close
if in_trade:
if trade_dir == 1:
pnl_pts = close[-1] - entry_price
else:
pnl_pts = entry_price - close[-1]
# Scale PnL proportionally
sl_dist_trade = abs(entry_price - sl_price)
if sl_dist_trade > 0:
pnl = risk_amount * (pnl_pts / sl_dist_trade)
else:
pnl = 0.0
balance += pnl
trades.append({
"entry_bar": trade_entry_bar,
"exit_bar": len(close) - 1,
"direction": "BUY" if trade_dir == 1 else "SELL",
"entry_price": entry_price,
"sl_price": sl_price,
"tp_price": tp_price,
"exit_price": close[-1],
"pnl": pnl,
"result": "OPEN_CLOSE",
"balance_after": balance,
"time_entry": df["time"].iloc[trade_entry_bar] if "time" in df.columns else None,
"time_exit": df["time"].iloc[-1] if "time" in df.columns else None,
})
equity_curve[-1] = balance
print(f"[INFO] Backtest complete: {len(trades)} trades, "
f"final balance: ${balance:,.2f}")
return {
"trades": trades,
"equity_curve": equity_curve,
"final_balance": balance,
}
def _compute_atr_vec(high, low, close, period):
"""Vectorized ATR for the backtester."""
n = len(high)
tr = np.empty(n, dtype=np.float64)
tr[0] = high[0] - low[0]
for i in range(1, n):
tr[i] = max(high[i] - low[i],
abs(high[i] - close[i - 1]),
abs(low[i] - close[i - 1]))
atr = np.empty(n, dtype=np.float64)
atr[:] = np.nan
if period <= n:
atr[period - 1] = np.mean(tr[:period])
alpha = 1.0 / period
for i in range(period, n):
atr[i] = atr[i - 1] * (1 - alpha) + tr[i] * alpha
return atr
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