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feat: implement LightGBM model improvement plan with feature normalization and walk-forward validation

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- Added a new markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation.
- Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes.
- Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance.
- Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility.
- Adjusted model training parameters for better performance and included detailed validation results in the training log.
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2026-03-01 22:02:32 +09:00
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# LightGBM 예측력 개선 구현 계획
> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
**Goal:** 현재 AUC 0.54 수준의 LightGBM 모델을 피처 정규화 + 강한 시간 가중치 + Walk-Forward 검증 세 가지를 순서대로 적용해 AUC 0.57+ 로 끌어올린다.
**Architecture:**
- `src/dataset_builder.py`에 rolling z-score 정규화를 추가해 레짐 변화에 강한 피처를 만든다.
- `scripts/train_model.py`에 Walk-Forward 검증 루프를 추가해 실제 예측력을 정확히 측정한다.
- 1년치 `combined_1m.parquet` 데이터를 decay=4.0 이상의 강한 시간 가중치로 학습해 샘플 수와 최신성을 동시에 확보한다.
**Tech Stack:** LightGBM, pandas, numpy, scikit-learn, Python 3.13
---
## 배경: 현재 문제 진단 결과
| 데이터 | 구간별 독립 AUC | 전체 80/20 AUC |
|--------|----------------|----------------|
| combined 1년 | 0.49~0.51 (전 구간 동일) | 0.49 |
| xrpusdt 3개월 | 0.49~0.58 (구간 편차 큼) | 0.54 |
**핵심 원인 두 가지:**
1. `xrp_btc_rs` 같은 절대값 피처가 Q1=0.86 → Q4=3.68로 4배 변동 → 모델이 스케일 변화에 혼란
2. 학습셋(과거)이 검증셋(최근)을 설명 못 함 → Walk-Forward로 실제 예측력 측정 필요
---
## Task 1: 피처 정규화 개선 (rolling z-score)
**Files:**
- Modify: `src/dataset_builder.py` — `_calc_features_vectorized()` 함수 내부
**목표:** 절대값 피처(`atr_pct`, `vol_ratio`, `xrp_btc_rs`, `xrp_eth_rs`, `ret_1/3/5`, `btc_ret_1/3/5`, `eth_ret_1/3/5`)를 rolling 200 window z-score로 정규화해서 레짐 변화에 무관하게 만든다.
**Step 1: 정규화 헬퍼 함수 추가**
`_calc_features_vectorized()` 함수 시작 부분에 추가:
```python
def _rolling_zscore(arr: np.ndarray, window: int = 200) -> np.ndarray:
"""rolling window z-score 정규화. window 미만 구간은 0으로 채운다."""
s = pd.Series(arr)
mean = s.rolling(window, min_periods=window).mean()
std = s.rolling(window, min_periods=window).std()
z = (s - mean) / std.replace(0, np.nan)
return z.fillna(0).values.astype(np.float32)
```
**Step 2: 절대값 피처에 정규화 적용**
`result` DataFrame 생성 시 다음 피처를 정규화 버전으로 교체:
```python
# 기존
"atr_pct": atr_pct.astype(np.float32),
"vol_ratio": vol_ratio.astype(np.float32),
"ret_1": ret_1.astype(np.float32),
"ret_3": ret_3.astype(np.float32),
"ret_5": ret_5.astype(np.float32),
# 변경 후
"atr_pct": _rolling_zscore(atr_pct),
"vol_ratio": _rolling_zscore(vol_ratio),
"ret_1": _rolling_zscore(ret_1),
"ret_3": _rolling_zscore(ret_3),
"ret_5": _rolling_zscore(ret_5),
```
BTC/ETH 피처도 동일하게:
```python
"btc_ret_1": _rolling_zscore(btc_r1), "btc_ret_3": _rolling_zscore(btc_r3), ...
"xrp_btc_rs": _rolling_zscore(xrp_btc_rs), "xrp_eth_rs": _rolling_zscore(xrp_eth_rs),
```
**Step 3: 검증**
```bash
cd /Users/gihyeon/github/cointrader
.venv/bin/python -c "
from src.dataset_builder import generate_dataset_vectorized
import pandas as pd
df = pd.read_parquet('data/combined_1m.parquet')
base = ['open','high','low','close','volume']
btc = df[[c+'_btc' for c in base]].copy(); btc.columns = base
eth = df[[c+'_eth' for c in base]].copy(); eth.columns = base
ds = generate_dataset_vectorized(df[base].copy(), btc_df=btc, eth_df=eth, time_weight_decay=0)
print(ds[['atr_pct','vol_ratio','xrp_btc_rs']].describe())
"
```
기대 결과: `atr_pct`, `vol_ratio`, `xrp_btc_rs` 모두 mean≈0, std≈1 범위
---
## Task 2: Walk-Forward 검증 함수 추가
**Files:**
- Modify: `scripts/train_model.py` — `train()` 함수 뒤에 `walk_forward_auc()` 함수 추가 및 `main()` 에 `--wf` 플래그 추가
**목표:** 시계열 순서를 지키면서 n_splits번 학습/검증을 반복해 실제 미래 예측력의 평균 AUC를 측정한다.
**Step 1: walk_forward_auc 함수 추가**
`train()` 함수 바로 아래에 추가:
```python
def walk_forward_auc(
data_path: str,
time_weight_decay: float = 2.0,
n_splits: int = 5,
train_ratio: float = 0.6,
) -> None:
"""Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복."""
import warnings
from sklearn.metrics import roc_auc_score
print(f"\n=== Walk-Forward 검증 ({n_splits}폴드) ===")
df_raw = pd.read_parquet(data_path)
base_cols = ["open", "high", "low", "close", "volume"]
btc_df = eth_df = None
if "close_btc" in df_raw.columns:
btc_df = df_raw[[c + "_btc" for c in base_cols]].copy(); btc_df.columns = base_cols
if "close_eth" in df_raw.columns:
eth_df = df_raw[[c + "_eth" for c in base_cols]].copy(); eth_df.columns = base_cols
df = df_raw[base_cols].copy()
dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df,
time_weight_decay=time_weight_decay)
actual_feature_cols = [c for c in FEATURE_COLS if c in dataset.columns]
X = dataset[actual_feature_cols].values
y = dataset["label"].values
w = dataset["sample_weight"].values
n = len(dataset)
step = int(n * (1 - train_ratio) / n_splits)
train_end_start = int(n * train_ratio)
aucs = []
for i in range(n_splits):
tr_end = train_end_start + i * step
val_end = tr_end + step
if val_end > n:
break
X_tr, y_tr, w_tr = X[:tr_end], y[:tr_end], w[:tr_end]
X_val, y_val = X[tr_end:val_end], y[tr_end:val_end]
pos_idx = np.where(y_tr == 1)[0]
neg_idx = np.where(y_tr == 0)[0]
if len(neg_idx) > len(pos_idx):
np.random.seed(42)
neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
idx = np.sort(np.concatenate([pos_idx, neg_idx]))
model = lgb.LGBMClassifier(
n_estimators=500, learning_rate=0.05, num_leaves=31,
min_child_samples=15, subsample=0.8, colsample_bytree=0.8,
reg_alpha=0.05, reg_lambda=0.1, random_state=42, verbose=-1,
)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_tr[idx], y_tr[idx], sample_weight=w_tr[idx])
proba = model.predict_proba(X_val)[:, 1]
if len(np.unique(y_val)) < 2:
auc = 0.5
else:
auc = roc_auc_score(y_val, proba)
aucs.append(auc)
print(f" 폴드 {i+1}/{n_splits}: 학습={tr_end}, 검증={tr_end}~{val_end} ({step}개), AUC={auc:.4f}")
print(f"\n Walk-Forward 평균 AUC: {np.mean(aucs):.4f} ± {np.std(aucs):.4f}")
print(f" 폴드별: {[round(a,4) for a in aucs]}")
```
**Step 2: main()에 --wf 플래그 추가**
```python
parser.add_argument("--wf", action="store_true", help="Walk-Forward 검증 실행")
parser.add_argument("--wf-splits", type=int, default=5)
# args 처리 부분
if args.wf:
walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits)
else:
train(args.data, time_weight_decay=args.decay)
```
**Step 3: 검증 실행**
```bash
# xrpusdt 3개월 Walk-Forward
.venv/bin/python scripts/train_model.py --data data/xrpusdt_1m.parquet --decay 2.0 --wf
# combined 1년 Walk-Forward
.venv/bin/python scripts/train_model.py --data data/combined_1m.parquet --decay 2.0 --wf
```
기대 결과: 폴드별 AUC가 0.50~0.58 범위, 평균 0.52+
---
## Task 3: 강한 시간 가중치 + 1년 데이터 최적화
**Files:**
- Modify: `scripts/train_model.py` — `train()` 함수 내 `--decay` 기본값 및 권장값 주석
**목표:** `combined_1m.parquet`에서 decay=4.0~5.0으로 최근 3개월에 집중하되 1년치 패턴도 참고한다.
**Step 1: decay 값별 AUC 비교 스크립트 실행**
```bash
for decay in 1.0 2.0 3.0 4.0 5.0; do
echo "=== decay=$decay ==="
.venv/bin/python scripts/train_model.py --data data/combined_1m.parquet --decay $decay --wf --wf-splits 3 2>&1 | grep "Walk-Forward 평균"
done
```
**Step 2: 최적 decay 값으로 최종 학습**
Walk-Forward 평균 AUC가 가장 높은 decay 값으로:
```bash
.venv/bin/python scripts/train_model.py --data data/combined_1m.parquet --decay <최적값>
```
**Step 3: 결과 확인**
```bash
.venv/bin/python -c "import json; log=json.load(open('models/training_log.json')); [print(e) for e in log[-3:]]"
```
---
## 예상 결과
| 개선 단계 | 예상 AUC |
|-----------|---------|
| 현재 (3개월, 기본) | 0.54 |
| + rolling z-score 정규화 | 0.54~0.56 |
| + Walk-Forward로 정확한 측정 | 측정 정확도 향상 |
| + decay=4.0, 1년 데이터 | 0.55~0.58 |
---
## 주의사항
- `_rolling_zscore`는 `dataset_builder.py` 내부에서만 사용 (실시간 봇 경로 `ml_features.py`는 건드리지 않음)
- Walk-Forward는 `--wf` 플래그로만 실행, 기본 `train()`은 그대로 유지
- rolling window=200은 약 3~4시간치 1분봉 → 단기 레짐 변화 반영

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@@ -45,5 +45,95 @@
"samples": 3269,
"features": 21,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:46:29.599674",
"backend": "mlx",
"auc": 0.516,
"samples": 6470,
"train_sec": 1.3,
"time_weight_decay": 2.0,
"model_path": "models/mlx_filter.weights"
},
{
"date": "2026-03-01T21:50:12.449819",
"backend": "lgbm",
"auc": 0.4772,
"samples": 6470,
"features": 21,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:50:32.491318",
"backend": "lgbm",
"auc": 0.4943,
"samples": 6470,
"features": 21,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:50:48.665654",
"backend": "lgbm",
"auc": 0.4943,
"samples": 6470,
"features": 21,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:51:02.539565",
"backend": "lgbm",
"auc": 0.4943,
"samples": 6470,
"features": 21,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:51:09.830250",
"backend": "lgbm",
"auc": 0.4925,
"samples": 1716,
"features": 13,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:51:20.133303",
"backend": "lgbm",
"auc": 0.54,
"samples": 1716,
"features": 13,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:51:25.445363",
"backend": "lgbm",
"auc": 0.4943,
"samples": 6470,
"features": 21,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T21:52:24.296191",
"backend": "lgbm",
"auc": 0.54,
"samples": 1716,
"features": 13,
"time_weight_decay": 2.0,
"model_path": "models/lgbm_filter.pkl"
},
{
"date": "2026-03-01T22:00:34.737597",
"backend": "lgbm",
"auc": 0.5097,
"samples": 6470,
"features": 21,
"time_weight_decay": 3.0,
"model_path": "models/lgbm_filter.pkl"
}
]

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scripts/train_model.py
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@@ -190,7 +190,7 @@ def train(data_path: str, time_weight_decay: float = 2.0):
y_train, y_val = y.iloc[:split], y.iloc[split:]
w_train = w[:split]
# --- 클래스 불균형 처리: 언더샘플링 (가중치 인덱스 보존) ---
# --- 클래스 불균형 처리: 언더샘플링 (시간 가중치 인덱스 보존) ---
pos_idx = np.where(y_train == 1)[0]
neg_idx = np.where(y_train == 0)[0]
@@ -198,24 +198,25 @@ def train(data_path: str, time_weight_decay: float = 2.0):
np.random.seed(42)
neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
balanced_idx = np.concatenate([pos_idx, neg_idx])
np.random.shuffle(balanced_idx)
balanced_idx = np.sort(np.concatenate([pos_idx, neg_idx])) # 시간 순서 유지
X_train = X_train.iloc[balanced_idx]
y_train = y_train.iloc[balanced_idx]
w_train = w_train[balanced_idx]
print(f"\n언더샘플링 적용 후 학습 데이터: {len(X_train)}개 (양성={y_train.sum()}, 음성={(y_train==0).sum()})")
# --------------------------------------
print(f"\n언더샘플링 후 학습 데이터: {len(X_train)}개 (양성={y_train.sum()}, 음성={(y_train==0).sum()})")
print(f"검증 데이터: {len(X_val)}개 (양성={int(y_val.sum())}, 음성={int((y_val==0).sum())})")
# ---------------------------------------------------------------
model = lgb.LGBMClassifier(
n_estimators=300,
n_estimators=500,
learning_rate=0.05,
num_leaves=31,
min_child_samples=20,
min_child_samples=15,
subsample=0.8,
colsample_bytree=0.8,
class_weight="balanced",
reg_alpha=0.05,
reg_lambda=0.1,
random_state=42,
verbose=-1,
)
@@ -223,13 +224,26 @@ def train(data_path: str, time_weight_decay: float = 2.0):
X_train, y_train,
sample_weight=w_train,
eval_set=[(X_val, y_val)],
callbacks=[lgb.early_stopping(30, verbose=False), lgb.log_evaluation(50)],
eval_metric="auc",
callbacks=[
lgb.early_stopping(80, first_metric_only=True, verbose=False),
lgb.log_evaluation(50),
],
)
val_proba = model.predict_proba(X_val)[:, 1]
auc = roc_auc_score(y_val, val_proba)
print(f"\n검증 AUC: {auc:.4f}")
print(classification_report(y_val, (val_proba >= 0.60).astype(int)))
# 최적 임계값 탐색 (F1 기준)
thresholds = np.arange(0.40, 0.70, 0.05)
best_thr, best_f1 = 0.50, 0.0
for thr in thresholds:
pred = (val_proba >= thr).astype(int)
from sklearn.metrics import f1_score
f1 = f1_score(y_val, pred, zero_division=0)
if f1 > best_f1:
best_f1, best_thr = f1, thr
print(f"\n검증 AUC: {auc:.4f} | 최적 임계값: {best_thr:.2f} (F1={best_f1:.3f})")
print(classification_report(y_val, (val_proba >= best_thr).astype(int), zero_division=0))
if MODEL_PATH.exists():
import shutil
@@ -259,6 +273,88 @@ def train(data_path: str, time_weight_decay: float = 2.0):
return auc
def walk_forward_auc(
data_path: str,
time_weight_decay: float = 2.0,
n_splits: int = 5,
train_ratio: float = 0.6,
) -> None:
"""Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복.
시계열 순서를 지키면서 매 폴드마다 학습 구간을 늘려가며 검증한다.
실제 미래 예측력의 평균 AUC를 측정하는 데 사용한다.
"""
import warnings
print(f"\n=== Walk-Forward 검증 ({n_splits}폴드, decay={time_weight_decay}) ===")
df_raw = pd.read_parquet(data_path)
base_cols = ["open", "high", "low", "close", "volume"]
btc_df = eth_df = None
if "close_btc" in df_raw.columns:
btc_df = df_raw[[c + "_btc" for c in base_cols]].copy()
btc_df.columns = base_cols
if "close_eth" in df_raw.columns:
eth_df = df_raw[[c + "_eth" for c in base_cols]].copy()
eth_df.columns = base_cols
df = df_raw[base_cols].copy()
dataset = generate_dataset_vectorized(
df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay
)
actual_feature_cols = [c for c in FEATURE_COLS if c in dataset.columns]
X = dataset[actual_feature_cols].values
y = dataset["label"].values
w = dataset["sample_weight"].values
n = len(dataset)
step = max(1, int(n * (1 - train_ratio) / n_splits))
train_end_start = int(n * train_ratio)
aucs = []
for i in range(n_splits):
tr_end = train_end_start + i * step
val_end = tr_end + step
if val_end > n:
break
X_tr, y_tr, w_tr = X[:tr_end], y[:tr_end], w[:tr_end]
X_val, y_val = X[tr_end:val_end], y[tr_end:val_end]
pos_idx = np.where(y_tr == 1)[0]
neg_idx = np.where(y_tr == 0)[0]
if len(neg_idx) > len(pos_idx):
np.random.seed(42)
neg_idx = np.random.choice(neg_idx, size=len(pos_idx), replace=False)
idx = np.sort(np.concatenate([pos_idx, neg_idx]))
model = lgb.LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
num_leaves=31,
min_child_samples=15,
subsample=0.8,
colsample_bytree=0.8,
reg_alpha=0.05,
reg_lambda=0.1,
random_state=42,
verbose=-1,
)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_tr[idx], y_tr[idx], sample_weight=w_tr[idx])
proba = model.predict_proba(X_val)[:, 1]
auc = roc_auc_score(y_val, proba) if len(np.unique(y_val)) > 1 else 0.5
aucs.append(auc)
print(
f" 폴드 {i+1}/{n_splits}: 학습={tr_end}개, "
f"검증={tr_end}~{val_end} ({step}개), AUC={auc:.4f}"
)
print(f"\n Walk-Forward 평균 AUC: {np.mean(aucs):.4f} ± {np.std(aucs):.4f}")
print(f" 폴드별: {[round(a, 4) for a in aucs]}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", default="data/combined_1m.parquet")
@@ -266,8 +362,14 @@ def main():
"--decay", type=float, default=2.0,
help="시간 가중치 감쇠 강도 (0=균등, 2.0=최신이 ~7.4배 높음)",
)
parser.add_argument("--wf", action="store_true", help="Walk-Forward 검증 실행")
parser.add_argument("--wf-splits", type=int, default=5, help="Walk-Forward 폴드 수")
args = parser.parse_args()
train(args.data, time_weight_decay=args.decay)
if args.wf:
walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits)
else:
train(args.data, time_weight_decay=args.decay)
if __name__ == "__main__":

42
src/dataset_builder.py
View File

@@ -115,6 +115,16 @@ def _calc_signals(d: pd.DataFrame) -> np.ndarray:
return signal_arr
def _rolling_zscore(arr: np.ndarray, window: int = 200) -> np.ndarray:
"""rolling window z-score 정규화. window 미만 구간은 0으로 채운다.
절대값 피처(수익률, ATR 등)를 레짐 변화에 무관하게 만든다."""
s = pd.Series(arr.astype(np.float64))
mean = s.rolling(window, min_periods=window).mean()
std = s.rolling(window, min_periods=window).std()
z = (s - mean) / std.where(std > 0, other=np.nan)
return z.fillna(0).values.astype(np.float32)
def _calc_features_vectorized(
d: pd.DataFrame,
signal_arr: np.ndarray,
@@ -158,6 +168,13 @@ def _calc_features_vectorized(
ret_3 = close.pct_change(3).fillna(0).values
ret_5 = close.pct_change(5).fillna(0).values
# 절대값 피처를 rolling z-score로 정규화 (레짐 변화에 강하게)
atr_pct_z = _rolling_zscore(atr_pct)
vol_ratio_z = _rolling_zscore(vol_ratio)
ret_1_z = _rolling_zscore(ret_1)
ret_3_z = _rolling_zscore(ret_3)
ret_5_z = _rolling_zscore(ret_5)
prev_macd = macd.shift(1).fillna(0).values
prev_macd_sig = macd_sig.shift(1).fillna(0).values
@@ -190,11 +207,11 @@ def _calc_features_vectorized(
"ema_align": ema_align,
"stoch_k": stoch_k.values.astype(np.float32),
"stoch_d": stoch_d.values.astype(np.float32),
"atr_pct": atr_pct.astype(np.float32),
"vol_ratio": vol_ratio.astype(np.float32),
"ret_1": ret_1.astype(np.float32),
"ret_3": ret_3.astype(np.float32),
"ret_5": ret_5.astype(np.float32),
"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": strength,
"side": side,
"_signal": signal_arr, # 레이블 계산용 임시 컬럼
@@ -223,13 +240,18 @@ def _calc_features_vectorized(
eth_r5 = _align(eth_ret_5, n).astype(np.float32)
xrp_r1 = ret_1.astype(np.float32)
xrp_btc_rs = np.where(btc_r1 != 0, xrp_r1 / btc_r1, 0.0).astype(np.float32)
xrp_eth_rs = np.where(eth_r1 != 0, xrp_r1 / eth_r1, 0.0).astype(np.float32)
xrp_btc_rs_raw = np.where(btc_r1 != 0, xrp_r1 / btc_r1, 0.0).astype(np.float32)
xrp_eth_rs_raw = np.where(eth_r1 != 0, xrp_r1 / eth_r1, 0.0).astype(np.float32)
extra = pd.DataFrame({
"btc_ret_1": btc_r1, "btc_ret_3": btc_r3, "btc_ret_5": btc_r5,
"eth_ret_1": eth_r1, "eth_ret_3": eth_r3, "eth_ret_5": eth_r5,
"xrp_btc_rs": xrp_btc_rs, "xrp_eth_rs": xrp_eth_rs,
"btc_ret_1": _rolling_zscore(btc_r1),
"btc_ret_3": _rolling_zscore(btc_r3),
"btc_ret_5": _rolling_zscore(btc_r5),
"eth_ret_1": _rolling_zscore(eth_r1),
"eth_ret_3": _rolling_zscore(eth_r3),
"eth_ret_5": _rolling_zscore(eth_r5),
"xrp_btc_rs": _rolling_zscore(xrp_btc_rs_raw),
"xrp_eth_rs": _rolling_zscore(xrp_eth_rs_raw),
}, index=d.index)
result = pd.concat([result, extra], axis=1)