02e41881ac
- Add independent backtest engine (backtester.py) with walk-forward support - Add backtest sanity check validator (backtest_validator.py) - Add CLI tools: run_backtest.py, strategy_sweep.py (with --combined mode) - Fix train-serve skew: unify feature z-score normalization (ml_features.py) - Add strategy params (SL/TP ATR mult, ADX filter, volume multiplier) to config.py, indicators.py, dataset_builder.py, bot.py, backtester.py - Fix WalkForwardBacktester not propagating strategy params to test folds - Update production defaults: SL=2.0x, TP=2.0x, ADX=25, Vol=2.5 (3-symbol combined PF: 0.71 → 1.24, MDD: 65.9% → 17.1%) - Retrain ML models with new strategy parameters Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
310 lines
12 KiB
Python
310 lines
12 KiB
Python
import pandas as pd
|
||
import numpy as np
|
||
|
||
FEATURE_COLS = [
|
||
"rsi", "macd_hist", "bb_pct", "ema_align",
|
||
"stoch_k", "stoch_d", "atr_pct", "vol_ratio",
|
||
"ret_1", "ret_3", "ret_5", "signal_strength", "side",
|
||
"btc_ret_1", "btc_ret_3", "btc_ret_5",
|
||
"eth_ret_1", "eth_ret_3", "eth_ret_5",
|
||
"xrp_btc_rs", "xrp_eth_rs",
|
||
# 시장 미시구조: OI 변화율(z-score), 펀딩비(z-score)
|
||
"oi_change", "funding_rate",
|
||
# OI 파생 피처
|
||
"oi_change_ma5", "oi_price_spread",
|
||
"adx",
|
||
]
|
||
|
||
# rolling z-score 윈도우 (학습과 동일)
|
||
_ZSCORE_WINDOW = 288 # 일반 피처: 15분봉 × 288 = 3일
|
||
_ZSCORE_WINDOW_OI = 96 # OI/펀딩비: 15분봉 × 96 = 1일
|
||
|
||
|
||
def _calc_ret(closes: pd.Series, n: int) -> float:
|
||
"""n캔들 전 대비 수익률. 데이터 부족 시 0.0."""
|
||
if len(closes) < n + 1:
|
||
return 0.0
|
||
prev = closes.iloc[-(n + 1)]
|
||
return (closes.iloc[-1] - prev) / prev if prev != 0 else 0.0
|
||
|
||
|
||
def _calc_rs(xrp_ret: float, other_ret: float) -> float:
|
||
"""상대강도 = xrp_ret / other_ret. 분모 0이면 0.0."""
|
||
if other_ret == 0.0:
|
||
return 0.0
|
||
return xrp_ret / other_ret
|
||
|
||
|
||
def _rolling_zscore_last(arr: np.ndarray, window: int = _ZSCORE_WINDOW) -> float:
|
||
"""배열의 마지막 값에 대한 rolling z-score를 반환한다.
|
||
학습(dataset_builder._rolling_zscore)과 동일한 로직."""
|
||
s = pd.Series(arr, dtype=np.float64)
|
||
r = s.rolling(window=window, min_periods=1)
|
||
mean = r.mean().iloc[-1]
|
||
std = r.std(ddof=0).iloc[-1]
|
||
if std < 1e-8:
|
||
std = 1e-8
|
||
return float((s.iloc[-1] - mean) / std)
|
||
|
||
|
||
def build_features(
|
||
df: pd.DataFrame,
|
||
signal: str,
|
||
btc_df: pd.DataFrame | None = None,
|
||
eth_df: pd.DataFrame | None = None,
|
||
oi_change: float | None = None,
|
||
funding_rate: float | None = None,
|
||
oi_change_ma5: float | None = None,
|
||
oi_price_spread: float | None = None,
|
||
) -> pd.Series:
|
||
"""
|
||
[Deprecated] raw 값 기반 피처. 하위 호환용으로 유지.
|
||
신규 코드는 build_features_aligned()를 사용할 것.
|
||
"""
|
||
last = df.iloc[-1]
|
||
close = last["close"]
|
||
|
||
bb_upper = last.get("bb_upper", close)
|
||
bb_lower = last.get("bb_lower", close)
|
||
bb_range = bb_upper - bb_lower
|
||
bb_pct = (close - bb_lower) / bb_range if bb_range > 0 else 0.5
|
||
|
||
ema9 = last.get("ema9", close)
|
||
ema21 = last.get("ema21", close)
|
||
ema50 = last.get("ema50", close)
|
||
if ema9 > ema21 > ema50:
|
||
ema_align = 1
|
||
elif ema9 < ema21 < ema50:
|
||
ema_align = -1
|
||
else:
|
||
ema_align = 0
|
||
|
||
atr = last.get("atr", 0)
|
||
atr_pct = atr / close if close > 0 else 0
|
||
|
||
vol_ma20 = last.get("vol_ma20", last.get("volume", 1))
|
||
vol_ratio = last["volume"] / vol_ma20 if vol_ma20 > 0 else 1.0
|
||
|
||
closes = df["close"]
|
||
ret_1 = _calc_ret(closes, 1)
|
||
ret_3 = _calc_ret(closes, 3)
|
||
ret_5 = _calc_ret(closes, 5)
|
||
|
||
prev = df.iloc[-2] if len(df) >= 2 else last
|
||
strength = 0
|
||
rsi = last.get("rsi", 50)
|
||
macd = last.get("macd", 0)
|
||
macd_sig = last.get("macd_signal", 0)
|
||
prev_macd = prev.get("macd", 0)
|
||
prev_macd_sig = prev.get("macd_signal", 0)
|
||
stoch_k = last.get("stoch_k", 50)
|
||
stoch_d = last.get("stoch_d", 50)
|
||
|
||
if signal == "LONG":
|
||
if rsi < 35: strength += 1
|
||
if prev_macd < prev_macd_sig and macd > macd_sig: strength += 2
|
||
if close < last.get("bb_lower", close): strength += 1
|
||
if ema_align == 1: strength += 1
|
||
if stoch_k < 20 and stoch_k > stoch_d: strength += 1
|
||
else:
|
||
if rsi > 65: strength += 1
|
||
if prev_macd > prev_macd_sig and macd < macd_sig: strength += 2
|
||
if close > last.get("bb_upper", close): strength += 1
|
||
if ema_align == -1: strength += 1
|
||
if stoch_k > 80 and stoch_k < stoch_d: strength += 1
|
||
|
||
base = {
|
||
"rsi": float(rsi),
|
||
"macd_hist": float(last.get("macd_hist", 0)),
|
||
"bb_pct": float(bb_pct),
|
||
"ema_align": float(ema_align),
|
||
"stoch_k": float(stoch_k),
|
||
"stoch_d": float(last.get("stoch_d", 50)),
|
||
"atr_pct": float(atr_pct),
|
||
"vol_ratio": float(vol_ratio),
|
||
"ret_1": float(ret_1),
|
||
"ret_3": float(ret_3),
|
||
"ret_5": float(ret_5),
|
||
"signal_strength": float(strength),
|
||
"side": 1.0 if signal == "LONG" else 0.0,
|
||
}
|
||
|
||
if btc_df is not None and eth_df is not None:
|
||
btc_ret_1 = _calc_ret(btc_df["close"], 1)
|
||
btc_ret_3 = _calc_ret(btc_df["close"], 3)
|
||
btc_ret_5 = _calc_ret(btc_df["close"], 5)
|
||
eth_ret_1 = _calc_ret(eth_df["close"], 1)
|
||
eth_ret_3 = _calc_ret(eth_df["close"], 3)
|
||
eth_ret_5 = _calc_ret(eth_df["close"], 5)
|
||
|
||
base.update({
|
||
"btc_ret_1": float(btc_ret_1),
|
||
"btc_ret_3": float(btc_ret_3),
|
||
"btc_ret_5": float(btc_ret_5),
|
||
"eth_ret_1": float(eth_ret_1),
|
||
"eth_ret_3": float(eth_ret_3),
|
||
"eth_ret_5": float(eth_ret_5),
|
||
"xrp_btc_rs": float(_calc_rs(ret_1, btc_ret_1)),
|
||
"xrp_eth_rs": float(_calc_rs(ret_1, eth_ret_1)),
|
||
})
|
||
|
||
# 실시간에서 실제 값이 제공되면 사용, 없으면 0으로 채운다
|
||
base["oi_change"] = float(oi_change) if oi_change is not None else 0.0
|
||
base["funding_rate"] = float(funding_rate) if funding_rate is not None else 0.0
|
||
base["oi_change_ma5"] = float(oi_change_ma5) if oi_change_ma5 is not None else 0.0
|
||
base["oi_price_spread"] = float(oi_price_spread) if oi_price_spread is not None else 0.0
|
||
base["adx"] = float(last.get("adx", 0))
|
||
|
||
return pd.Series(base)
|
||
|
||
|
||
def build_features_aligned(
|
||
df: pd.DataFrame,
|
||
signal: str,
|
||
btc_df: pd.DataFrame | None = None,
|
||
eth_df: pd.DataFrame | None = None,
|
||
oi_change: float | None = None,
|
||
funding_rate: float | None = None,
|
||
oi_change_ma5: float | None = None,
|
||
oi_price_spread: float | None = None,
|
||
) -> pd.Series:
|
||
"""
|
||
학습(dataset_builder._calc_features_vectorized)과 동일한 rolling z-score를
|
||
적용한 피처를 반환한다. train-serve skew를 방지한다.
|
||
|
||
df: 지표가 이미 계산된 DataFrame (최소 60캔들 이상)
|
||
signal: "LONG" | "SHORT"
|
||
"""
|
||
last = df.iloc[-1]
|
||
close_series = df["close"]
|
||
close = float(close_series.iloc[-1])
|
||
|
||
# --- raw 값 계산 (z-score 전) ---
|
||
bb_upper = df["bb_upper"] if "bb_upper" in df.columns else pd.Series(close, index=df.index)
|
||
bb_lower = df["bb_lower"] if "bb_lower" in df.columns else pd.Series(close, index=df.index)
|
||
bb_range = bb_upper - bb_lower
|
||
bb_pct_series = (close_series - bb_lower) / (bb_range + 1e-8)
|
||
|
||
ema9 = df.get("ema9", close_series)
|
||
ema21 = df.get("ema21", close_series)
|
||
ema50 = df.get("ema50", close_series)
|
||
|
||
ema_align_arr = np.where(
|
||
(ema9 > ema21) & (ema21 > ema50), 1,
|
||
np.where((ema9 < ema21) & (ema21 < ema50), -1, 0)
|
||
).astype(np.float32)
|
||
|
||
atr_series = df["atr"] if "atr" in df.columns else pd.Series(0.0, index=df.index)
|
||
atr_pct_arr = (atr_series / (close_series + 1e-8)).values
|
||
|
||
volume = df["volume"]
|
||
vol_ma20 = df["vol_ma20"] if "vol_ma20" in df.columns else pd.Series(1.0, index=df.index)
|
||
vol_ratio_arr = (volume / (vol_ma20 + 1e-8)).values
|
||
|
||
ret_1_arr = close_series.pct_change(1).fillna(0).values
|
||
ret_3_arr = close_series.pct_change(3).fillna(0).values
|
||
ret_5_arr = close_series.pct_change(5).fillna(0).values
|
||
|
||
# z-score 적용 (학습과 동일)
|
||
atr_pct_z = _rolling_zscore_last(atr_pct_arr)
|
||
vol_ratio_z = _rolling_zscore_last(vol_ratio_arr)
|
||
ret_1_z = _rolling_zscore_last(ret_1_arr)
|
||
ret_3_z = _rolling_zscore_last(ret_3_arr)
|
||
ret_5_z = _rolling_zscore_last(ret_5_arr)
|
||
|
||
# signal_strength
|
||
rsi = float(last.get("rsi", 50))
|
||
macd_val = float(last.get("macd", 0))
|
||
macd_sig_val = float(last.get("macd_signal", 0))
|
||
stoch_k = float(last.get("stoch_k", 50))
|
||
stoch_d = float(last.get("stoch_d", 50))
|
||
prev = df.iloc[-2] if len(df) >= 2 else last
|
||
prev_macd = float(prev.get("macd", 0))
|
||
prev_macd_sig = float(prev.get("macd_signal", 0))
|
||
|
||
strength = 0
|
||
if signal == "LONG":
|
||
if rsi < 35: strength += 1
|
||
if prev_macd < prev_macd_sig and macd_val > macd_sig_val: strength += 2
|
||
if close < float(last.get("bb_lower", close)): strength += 1
|
||
if ema_align_arr[-1] == 1: strength += 1
|
||
if stoch_k < 20 and stoch_k > stoch_d: strength += 1
|
||
else:
|
||
if rsi > 65: strength += 1
|
||
if prev_macd > prev_macd_sig and macd_val < macd_sig_val: strength += 2
|
||
if close > float(last.get("bb_upper", close)): strength += 1
|
||
if ema_align_arr[-1] == -1: strength += 1
|
||
if stoch_k > 80 and stoch_k < stoch_d: strength += 1
|
||
|
||
# ADX z-score
|
||
adx_arr = df["adx"].values.astype(np.float64) if "adx" in df.columns else np.zeros(len(df))
|
||
adx_z = _rolling_zscore_last(adx_arr)
|
||
|
||
base = {
|
||
"rsi": rsi,
|
||
"macd_hist": float(last.get("macd_hist", 0)),
|
||
"bb_pct": float(bb_pct_series.iloc[-1]),
|
||
"ema_align": float(ema_align_arr[-1]),
|
||
"stoch_k": stoch_k,
|
||
"stoch_d": stoch_d,
|
||
"atr_pct": atr_pct_z,
|
||
"vol_ratio": vol_ratio_z,
|
||
"ret_1": ret_1_z,
|
||
"ret_3": ret_3_z,
|
||
"ret_5": ret_5_z,
|
||
"signal_strength": float(strength),
|
||
"side": 1.0 if signal == "LONG" else 0.0,
|
||
}
|
||
|
||
# BTC/ETH 상관 피처 (z-score)
|
||
if btc_df is not None and eth_df is not None:
|
||
btc_r1 = btc_df["close"].pct_change(1).fillna(0).values
|
||
btc_r3 = btc_df["close"].pct_change(3).fillna(0).values
|
||
btc_r5 = btc_df["close"].pct_change(5).fillna(0).values
|
||
eth_r1 = eth_df["close"].pct_change(1).fillna(0).values
|
||
eth_r3 = eth_df["close"].pct_change(3).fillna(0).values
|
||
eth_r5 = eth_df["close"].pct_change(5).fillna(0).values
|
||
|
||
# 길이 맞춤 (btc/eth가 더 길 수 있음)
|
||
n = len(df)
|
||
def _align(arr):
|
||
if len(arr) >= n:
|
||
return arr[-n:]
|
||
return np.concatenate([np.zeros(n - len(arr)), arr])
|
||
|
||
btc_r1 = _align(btc_r1)
|
||
btc_r3 = _align(btc_r3)
|
||
btc_r5 = _align(btc_r5)
|
||
eth_r1 = _align(eth_r1)
|
||
eth_r3 = _align(eth_r3)
|
||
eth_r5 = _align(eth_r5)
|
||
|
||
# 상대강도 (raw → z-score)
|
||
xrp_r1 = ret_1_arr.astype(np.float32)
|
||
btc_r1_f = btc_r1.astype(np.float32)
|
||
eth_r1_f = eth_r1.astype(np.float32)
|
||
rs_btc = np.divide(xrp_r1, btc_r1_f, out=np.zeros_like(xrp_r1), where=(btc_r1_f != 0))
|
||
rs_eth = np.divide(xrp_r1, eth_r1_f, out=np.zeros_like(xrp_r1), where=(eth_r1_f != 0))
|
||
|
||
base.update({
|
||
"btc_ret_1": _rolling_zscore_last(btc_r1),
|
||
"btc_ret_3": _rolling_zscore_last(btc_r3),
|
||
"btc_ret_5": _rolling_zscore_last(btc_r5),
|
||
"eth_ret_1": _rolling_zscore_last(eth_r1),
|
||
"eth_ret_3": _rolling_zscore_last(eth_r3),
|
||
"eth_ret_5": _rolling_zscore_last(eth_r5),
|
||
"xrp_btc_rs": _rolling_zscore_last(rs_btc),
|
||
"xrp_eth_rs": _rolling_zscore_last(rs_eth),
|
||
})
|
||
|
||
# OI/펀딩비 z-score (실시간 값이 제공되면 히스토리 끝에 추가하여 z-score)
|
||
# 서빙 시 OI/펀딩비 히스토리가 없으므로 단일 값 → z-score 불가, NaN 처리
|
||
# LightGBM은 NaN을 자체 처리함
|
||
base["oi_change"] = float(oi_change) if oi_change is not None else np.nan
|
||
base["funding_rate"] = float(funding_rate) if funding_rate is not None else np.nan
|
||
base["oi_change_ma5"] = float(oi_change_ma5) if oi_change_ma5 is not None else np.nan
|
||
base["oi_price_spread"] = float(oi_price_spread) if oi_price_spread is not None else np.nan
|
||
base["adx"] = adx_z
|
||
|
||
return pd.Series(base)
|