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feat: add OI derived features (oi_change_ma5, oi_price_spread)

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2026-03-04 20:32:51 +09:00
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CLAUDE.md
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@@ -47,7 +47,7 @@ bash scripts/deploy_model.sh
**5-layer data flow on each 15m candle close:**
1. `src/data_stream.py` — Combined WebSocket for XRP/BTC/ETH, deque buffers (200 candles each)
2. `src/indicators.py` — RSI, MACD, BB, EMA, StochRSI, ATR; weighted signal aggregation → LONG/SHORT/HOLD
3. `src/ml_filter.py` + `src/ml_features.py` — 24-feature extraction (ADX 포함), ONNX priority > LightGBM fallback, threshold ≥ 0.55
3. `src/ml_filter.py` + `src/ml_features.py` — 26-feature extraction (ADX + OI 파생 피처 포함), ONNX priority > LightGBM fallback, threshold ≥ 0.55
4. `src/exchange.py` + `src/risk_manager.py` — Dynamic margin, MARKET orders with SL/TP, daily loss limit (5%)
5. `src/user_data_stream.py` + `src/notifier.py` — Real-time TP/SL detection via WebSocket, Discord webhooks
@@ -116,3 +116,4 @@ All design documents and implementation plans are stored in `docs/plans/` with t
| 2026-03-03 | `position-monitor-logging` | Completed |
| 2026-03-03 | `adx-ml-feature-migration` (design + plan) | Completed |
| 2026-03-03 | `optuna-precision-objective-plan` | Pending |
| 2026-03-04 | `oi-derived-features` (design + plan) | In Progress |

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# OI 파생 피처 설계
## 목표
기존 `oi_change` 피처에 더해, OI 데이터에서 파생 피처 2개를 만들어 LightGBM 학습 데이터에 추가하고, 피처 추가 전후 검증셋 성능을 자동 비교한다.
## 제약사항
- Binance OI 히스토리 API는 최근 30일분만 제공
- 학습 데이터에서 OI 유효 구간 ≈ 2,880개 15분 캔들
- A/B 비교 결과는 방향성 참고용 (통계적 유의성 제한)
## 파생 피처
### 1. `oi_change_ma5`
- **계산**: OI 변화율의 5캔들(75분) 이동평균
- **의미**: OI 단기 추세. 급감/급증 노이즈 제거된 방향성
- **정규화**: rolling z-score (288캔들 윈도우, 기존 패턴 동일)
- **기존 `oi_change`와의 관계**: smoothed 버전. 상관관계 높을 수 있으나 LightGBM이 자연 선택. importance 낮으면 이후 제거
### 2. `oi_price_spread`
- **계산**: `rolling_zscore(oi_change) - rolling_zscore(price_ret_1)`
- **의미**: OI와 가격 움직임 간 괴리도 (연속값)
- 양수: OI가 가격 대비 강세 (자금 유입)
- 음수: OI가 가격 대비 약세 (자금 유출)
- **정규화**: 양쪽 입력이 이미 z-score이므로 추가 정규화 불필요
- **바이너리 대신 연속값 채택 이유**: sign() 기반 바이너리는 미미한 차이도 1/0으로 분류 → 노이즈 과잉. 연속값은 LightGBM이 분할점을 학습
## 수정 대상 파일
### dataset_builder.py
- OI 파생 피처 2개 계산 로직 추가
- 기존 `oi_change` z-score 결과를 재사용하여 `oi_change_ma5` 계산
- `oi_price_spread` = `oi_change` z-score - `ret_1` z-score
### ml_features.py
- `FEATURE_COLS`에 `oi_change_ma5`, `oi_price_spread` 추가 (24→26)
- `build_features()`에 실시간 계산 로직 추가
- `oi_change_ma5`: bot에서 전달받은 최근 5봉 OI MA
- `oi_price_spread`: 실시간 z-scored OI - z-scored price change
### train_model.py
- `--compare` 플래그 추가
- Baseline (기존 24피처) vs New (26피처) 자동 비교 출력:
- Precision, Recall, F1, AUC-ROC
- Feature importance top 10
- Best threshold
- 검증셋 크기 (n=XX) 및 "방향성 참고용" 경고
### bot.py
- OI 변화율 히스토리 deque(maxlen=5) 관리
- `_init_oi_history()`: 봇 시작 시 Binance OI hist API에서 최근 5봉 fetch → cold start 해결
- `_fetch_market_microstructure()` 확장: MA5 계산, price_spread 계산 후 build_features()에 전달
### exchange.py
- `get_oi_history(limit=5)`: 봇 초기화용 최근 OI 히스토리 fetch 메서드 추가
### scripts/collect_oi.py (신규)
- OI 장기 수집 스크립트
- 15분마다 cron 실행
- Binance `/fapi/v1/openInterest` 호출 → `data/oi_history.parquet`에 append
- 기존 fetch_history.py의 30일 데이터 보완용

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# OI 파생 피처 구현 계획
> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
**Goal:** OI 파생 피처 2개(`oi_change_ma5`, `oi_price_spread`)를 추가하고, 기존 대비 성능을 자동 비교하며, OI 장기 수집 스크립트를 만든다.
**Architecture:** dataset_builder.py에 파생 피처 계산 추가 → ml_features.py에 FEATURE_COLS/build_features 확장 → train_model.py에 --compare 플래그로 A/B 비교 → bot.py에 OI deque 히스토리 관리 및 cold start → scripts/collect_oi.py 신규
**Tech Stack:** Python, LightGBM, pandas, numpy, Binance REST API
---
### Task 1: dataset_builder.py — OI 파생 피처 계산
**Files:**
- Modify: `src/dataset_builder.py:277-291` (OI/FR 피처 계산 블록)
- Test: `tests/test_dataset_builder.py`
**Step 1: Write failing tests**
`tests/test_dataset_builder.py` 끝에 추가:
```python
def test_oi_derived_features_present():
"""OI 파생 피처 2개가 결과에 포함되어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 300
np.random.seed(42)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
"oi_change": np.concatenate([np.zeros(100), np.random.uniform(-0.05, 0.05, 200)]),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
assert "oi_change_ma5" in feat.columns, "oi_change_ma5 컬럼이 없음"
assert "oi_price_spread" in feat.columns, "oi_price_spread 컬럼이 없음"
def test_oi_derived_features_nan_when_no_oi():
"""oi_change 컬럼이 없으면 파생 피처도 nan이어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 200
np.random.seed(0)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
assert feat["oi_change_ma5"].isna().all(), "oi_change 컬럼 없을 때 oi_change_ma5는 전부 nan이어야 함"
assert feat["oi_price_spread"].isna().all(), "oi_change 컬럼 없을 때 oi_price_spread는 전부 nan이어야 함"
def test_oi_price_spread_is_continuous():
"""oi_price_spread는 바이너리가 아닌 연속값이어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 300
np.random.seed(42)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
"oi_change": np.random.uniform(-0.05, 0.05, n),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
valid = feat["oi_price_spread"].dropna()
assert len(valid.unique()) > 2, "oi_price_spread는 연속값이어야 함 (2개 초과 유니크 값)"
```
**Step 2: Run tests to verify they fail**
Run: `bash scripts/run_tests.sh -k "oi_derived"`
Expected: FAIL — `oi_change_ma5`, `oi_price_spread` 컬럼 없음
**Step 3: Implement in dataset_builder.py**
`src/dataset_builder.py:277-291` (기존 OI/FR 블록) 뒤에 파생 피처 추가:
```python
# OI 변화율 / 펀딩비 피처
# 컬럼 없으면 전체 nan, 있으면 0.0 구간(데이터 미제공 구간)을 nan으로 마스킹
if "oi_change" in d.columns:
oi_raw = np.where(d["oi_change"].values == 0.0, np.nan, d["oi_change"].values)
else:
oi_raw = np.full(len(d), np.nan)
if "funding_rate" in d.columns:
fr_raw = np.where(d["funding_rate"].values == 0.0, np.nan, d["funding_rate"].values)
else:
fr_raw = np.full(len(d), np.nan)
oi_z = _rolling_zscore(oi_raw.astype(np.float64), window=96)
result["oi_change"] = oi_z
result["funding_rate"] = _rolling_zscore(fr_raw.astype(np.float64), window=96)
# --- OI 파생 피처 ---
# 1. oi_change_ma5: OI 변화율의 5캔들 이동평균 (단기 추세)
oi_series = pd.Series(oi_raw.astype(np.float64))
oi_ma5_raw = oi_series.rolling(window=5, min_periods=1).mean().values
result["oi_change_ma5"] = _rolling_zscore(oi_ma5_raw, window=96)
# 2. oi_price_spread: z-scored OI 변화율 - z-scored 가격 수익률 (연속값)
# 양수: OI가 가격 대비 강세 (자금 유입)
# 음수: OI가 가격 대비 약세 (자금 유출)
result["oi_price_spread"] = oi_z - ret_1_z
```
주의: 기존 `oi_change`와 `funding_rate`의 window도 288→96으로 변경. `oi_z` 변수를 재사용하여 `oi_price_spread` 계산. `ret_1_z`는 이미 위에서 계산됨 (line 181).
**Step 4: Update OPTIONAL_COLS in generate_dataset_vectorized**
`src/dataset_builder.py:387` 수정:
```python
OPTIONAL_COLS = {"oi_change", "funding_rate", "oi_change_ma5", "oi_price_spread"}
```
**Step 5: Run tests to verify they pass**
Run: `bash scripts/run_tests.sh -k "oi_derived"`
Expected: 3 tests PASS
**Step 6: Run full test suite**
Run: `bash scripts/run_tests.sh`
Expected: All existing tests PASS (기존 oi_change/funding_rate 테스트 포함)
**Step 7: Commit**
```bash
git add src/dataset_builder.py tests/test_dataset_builder.py
git commit -m "feat: add oi_change_ma5 and oi_price_spread derived features to dataset builder"
```
---
### Task 2: ml_features.py — FEATURE_COLS 및 build_features() 확장
**Files:**
- Modify: `src/ml_features.py:4-15` (FEATURE_COLS), `src/ml_features.py:33-139` (build_features)
- Test: `tests/test_ml_features.py`
**Step 1: Write failing tests**
`tests/test_ml_features.py` 끝에 추가:
```python
def test_feature_cols_has_26_items():
from src.ml_features import FEATURE_COLS
assert len(FEATURE_COLS) == 26
def test_build_features_with_oi_derived_params():
"""oi_change_ma5, oi_price_spread 파라미터가 피처에 반영된다."""
xrp_df = _make_df(10, base_price=1.0)
btc_df = _make_df(10, base_price=50000.0)
eth_df = _make_df(10, base_price=3000.0)
features = build_features(
xrp_df, "LONG",
btc_df=btc_df, eth_df=eth_df,
oi_change=0.05, funding_rate=0.0002,
oi_change_ma5=0.03, oi_price_spread=0.12,
)
assert features["oi_change_ma5"] == pytest.approx(0.03)
assert features["oi_price_spread"] == pytest.approx(0.12)
def test_build_features_oi_derived_defaults_to_zero():
"""oi_change_ma5, oi_price_spread 미제공 시 0.0으로 채워진다."""
xrp_df = _make_df(10, base_price=1.0)
features = build_features(xrp_df, "LONG")
assert features["oi_change_ma5"] == pytest.approx(0.0)
assert features["oi_price_spread"] == pytest.approx(0.0)
```
기존 테스트 수정:
- `test_feature_cols_has_24_items` → 삭제 또는 숫자를 26으로 변경
- `test_build_features_with_btc_eth_has_24_features` → `assert len(features) == 26`
- `test_build_features_without_btc_eth_has_16_features` → `assert len(features) == 18`
**Step 2: Run tests to verify they fail**
Run: `bash scripts/run_tests.sh -k "test_feature_cols_has_26 or test_build_features_oi_derived"`
Expected: FAIL
**Step 3: Implement**
`src/ml_features.py` FEATURE_COLS 수정 (line 4-15):
```python
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",
]
```
`build_features()` 시그니처 수정 (line 33-40):
```python
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:
```
`build_features()` 끝부분 (line 134-138) 수정:
```python
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))
```
**Step 4: Run tests**
Run: `bash scripts/run_tests.sh -k "test_ml_features"`
Expected: All PASS
**Step 5: Run full test suite**
Run: `bash scripts/run_tests.sh`
Expected: All PASS (test_dataset_builder의 FEATURE_COLS 참조도 26개로 통과)
**Step 6: Commit**
```bash
git add src/ml_features.py tests/test_ml_features.py
git commit -m "feat: add oi_change_ma5 and oi_price_spread to FEATURE_COLS and build_features"
```
---
### Task 3: train_model.py — --compare A/B 비교 모드
**Files:**
- Modify: `scripts/train_model.py:425-452` (main, argparse)
- Test: 수동 실행 확인 (학습 스크립트는 통합 테스트)
**Step 1: Implement compare function**
`scripts/train_model.py`에 `compare()` 함수 추가 (train() 함수 뒤):
```python
def compare(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None):
"""기존 피처 vs OI 파생 피처 추가 버전 A/B 비교."""
print("=" * 70)
print(" OI 파생 피처 A/B 비교 (30일 데이터 기반, 방향성 참고용)")
print("=" * 70)
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()
if "oi_change" in df_raw.columns:
df["oi_change"] = df_raw["oi_change"]
if "funding_rate" in df_raw.columns:
df["funding_rate"] = df_raw["funding_rate"]
dataset = generate_dataset_vectorized(
df, btc_df=btc_df, eth_df=eth_df,
time_weight_decay=time_weight_decay,
negative_ratio=5,
)
if dataset.empty:
raise ValueError("데이터셋 생성 실패")
lgbm_params, weight_scale = _load_lgbm_params(tuned_params_path)
# Baseline: OI 파생 피처 제외
BASELINE_EXCLUDE = {"oi_change_ma5", "oi_price_spread"}
baseline_cols = [c for c in FEATURE_COLS if c in dataset.columns and c not in BASELINE_EXCLUDE]
new_cols = [c for c in FEATURE_COLS if c in dataset.columns]
results = {}
for label, cols in [("Baseline (24)", baseline_cols), ("New (26)", new_cols)]:
X = dataset[cols]
y = dataset["label"]
w = dataset["sample_weight"].values
source = dataset["source"].values if "source" in dataset.columns else np.full(len(X), "signal")
split = int(len(X) * 0.8)
X_tr, X_val = X.iloc[:split], X.iloc[split:]
y_tr, y_val = y.iloc[:split], y.iloc[split:]
w_tr = (w[:split] * weight_scale).astype(np.float32)
source_tr = source[:split]
balanced_idx = stratified_undersample(y_tr.values, source_tr, seed=42)
X_tr_b = X_tr.iloc[balanced_idx]
y_tr_b = y_tr.iloc[balanced_idx]
w_tr_b = w_tr[balanced_idx]
import warnings
model = lgb.LGBMClassifier(**lgbm_params, random_state=42, verbose=-1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_tr_b, y_tr_b, sample_weight=w_tr_b)
proba = model.predict_proba(X_val)[:, 1]
auc = roc_auc_score(y_val, proba) if len(np.unique(y_val)) > 1 else 0.5
precs, recs, thrs = precision_recall_curve(y_val, proba)
precs, recs = precs[:-1], recs[:-1]
valid_idx = np.where(recs >= 0.15)[0]
if len(valid_idx) > 0:
best_i = valid_idx[np.argmax(precs[valid_idx])]
thr, prec, rec = float(thrs[best_i]), float(precs[best_i]), float(recs[best_i])
else:
thr, prec, rec = 0.50, 0.0, 0.0
# Feature importance
imp = dict(zip(cols, model.feature_importances_))
top10 = sorted(imp.items(), key=lambda x: x[1], reverse=True)[:10]
results[label] = {
"auc": auc, "precision": prec, "recall": rec,
"threshold": thr, "n_val": len(y_val),
"n_val_pos": int(y_val.sum()), "top10": top10,
}
# 비교 테이블 출력
print(f"\n{'지표':<20} {'Baseline (24)':>15} {'New (26)':>15} {'Delta':>10}")
print("-" * 62)
for metric in ["auc", "precision", "recall", "threshold"]:
b = results["Baseline (24)"][metric]
n = results["New (26)"][metric]
d = n - b
sign = "+" if d > 0 else ""
print(f"{metric:<20} {b:>15.4f} {n:>15.4f} {sign}{d:>9.4f}")
n_val = results["Baseline (24)"]["n_val"]
n_pos = results["Baseline (24)"]["n_val_pos"]
print(f"\n검증셋: n={n_val} (양성={n_pos}, 음성={n_val - n_pos})")
print("⚠ 30일 데이터 기반 — 방향성 참고용\n")
print("Feature Importance Top 10 (New):")
for feat_name, imp_val in results["New (26)"]["top10"]:
marker = " ← NEW" if feat_name in BASELINE_EXCLUDE else ""
print(f" {feat_name:<25} {imp_val:>6}{marker}")
```
**Step 2: Add --compare flag to argparse**
`scripts/train_model.py` main() 함수의 argparse에 추가:
```python
parser.add_argument("--compare", action="store_true",
help="OI 파생 피처 추가 전후 A/B 성능 비교")
```
main() 분기에 추가:
```python
if args.compare:
compare(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params)
elif args.wf:
...
```
**Step 3: Commit**
```bash
git add scripts/train_model.py
git commit -m "feat: add --compare flag for OI derived features A/B comparison"
```
---
### Task 4: bot.py — OI deque 히스토리 및 실시간 파생 피처 공급
**Files:**
- Modify: `src/bot.py:15-31` (init), `src/bot.py:60-83` (fetch/calc), `src/bot.py:110-114,287-291` (build_features 호출)
- Modify: `src/exchange.py` (get_oi_history 추가)
- Test: `tests/test_bot.py`
**Step 1: Write failing tests**
`tests/test_bot.py` 끝에 추가:
```python
def test_bot_has_oi_history_deque(config):
"""봇이 OI 히스토리 deque를 가져야 한다."""
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
from collections import deque
assert isinstance(bot._oi_history, deque)
assert bot._oi_history.maxlen == 5
@pytest.mark.asyncio
async def test_init_oi_history_fills_deque(config):
"""_init_oi_history가 deque를 채워야 한다."""
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
bot.exchange.get_oi_history = AsyncMock(return_value=[0.01, -0.02, 0.03, -0.01, 0.02])
await bot._init_oi_history()
assert len(bot._oi_history) == 5
@pytest.mark.asyncio
async def test_fetch_microstructure_returns_derived_features(config):
"""_fetch_market_microstructure가 oi_change_ma5와 oi_price_spread를 반환해야 한다."""
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
bot.exchange.get_open_interest = AsyncMock(return_value=5000000.0)
bot.exchange.get_funding_rate = AsyncMock(return_value=0.0001)
bot._prev_oi = 4900000.0
bot._oi_history.extend([0.01, -0.02, 0.03, -0.01])
bot._latest_ret_1 = 0.01
result = await bot._fetch_market_microstructure()
assert len(result) == 4 # oi_change, funding_rate, oi_change_ma5, oi_price_spread
```
**Step 2: Run tests to verify they fail**
Run: `bash scripts/run_tests.sh -k "oi_history or fetch_microstructure_returns_derived"`
Expected: FAIL
**Step 3: Implement exchange.get_oi_history()**
`src/exchange.py`에 추가:
```python
async def get_oi_history(self, limit: int = 5) -> list[float]:
"""최근 OI 변화율 히스토리를 조회한다 (봇 초기화용). 실패 시 빈 리스트."""
loop = asyncio.get_event_loop()
try:
result = await loop.run_in_executor(
None,
lambda: self.client.futures_open_interest_hist(
symbol=self.config.symbol, period="15m", limit=limit + 1,
),
)
if len(result) < 2:
return []
oi_values = [float(r["sumOpenInterest"]) for r in result]
changes = []
for i in range(1, len(oi_values)):
if oi_values[i - 1] > 0:
changes.append((oi_values[i] - oi_values[i - 1]) / oi_values[i - 1])
else:
changes.append(0.0)
return changes
except Exception as e:
logger.warning(f"OI 히스토리 조회 실패 (무시): {e}")
return []
```
**Step 4: Implement bot.py changes**
`src/bot.py` `__init__` 수정:
```python
from collections import deque
# __init__에 추가:
self._oi_history: deque = deque(maxlen=5)
self._latest_ret_1: float = 0.0 # 최신 가격 수익률 (oi_price_spread용)
```
`_init_oi_history()` 추가:
```python
async def _init_oi_history(self) -> None:
"""봇 시작 시 최근 OI 변화율 히스토리를 조회하여 deque를 채운다."""
try:
changes = await self.exchange.get_oi_history(limit=5)
for c in changes:
self._oi_history.append(c)
if changes:
self._prev_oi = None # 다음 실시간 OI로 갱신
logger.info(f"OI 히스토리 초기화: {len(self._oi_history)}개")
except Exception as e:
logger.warning(f"OI 히스토리 초기화 실패 (무시): {e}")
```
`_fetch_market_microstructure()` 수정 — 4-tuple 반환:
```python
async def _fetch_market_microstructure(self) -> tuple[float, float, float, float]:
"""OI 변화율, 펀딩비, OI MA5, OI-가격 스프레드를 실시간으로 조회한다."""
oi_val, fr_val = await asyncio.gather(
self.exchange.get_open_interest(),
self.exchange.get_funding_rate(),
return_exceptions=True,
)
if isinstance(oi_val, (int, float)) and oi_val > 0:
oi_change = self._calc_oi_change(float(oi_val))
else:
oi_change = 0.0
fr_float = float(fr_val) if isinstance(fr_val, (int, float)) else 0.0
# OI 히스토리 업데이트 및 MA5 계산
self._oi_history.append(oi_change)
oi_ma5 = sum(self._oi_history) / len(self._oi_history) if self._oi_history else 0.0
# OI-가격 스프레드 (단순 차이, 실시간에서는 z-score 없이 raw)
oi_price_spread = oi_change - self._latest_ret_1
logger.debug(
f"OI={oi_val}, OI변화율={oi_change:.6f}, 펀딩비={fr_float:.6f}, "
f"OI_MA5={oi_ma5:.6f}, OI_Price_Spread={oi_price_spread:.6f}"
)
return oi_change, fr_float, oi_ma5, oi_price_spread
```
`process_candle()` 수정:
```python
# 캔들 마감 시 가격 수익률 계산 (oi_price_spread용)
if len(df) >= 2:
prev_close = df["close"].iloc[-2]
curr_close = df["close"].iloc[-1]
self._latest_ret_1 = (curr_close - prev_close) / prev_close if prev_close != 0 else 0.0
oi_change, funding_rate, oi_ma5, oi_price_spread = await self._fetch_market_microstructure()
```
모든 `build_features()` 호출에 새 파라미터 추가:
```python
features = build_features(
df_with_indicators, signal,
btc_df=btc_df, eth_df=eth_df,
oi_change=oi_change, funding_rate=funding_rate,
oi_change_ma5=oi_ma5, oi_price_spread=oi_price_spread,
)
```
`_close_and_reenter()` 시그니처도 확장:
```python
async def _close_and_reenter(
self,
position: dict,
signal: str,
df,
btc_df=None,
eth_df=None,
oi_change: float = 0.0,
funding_rate: float = 0.0,
oi_change_ma5: float = 0.0,
oi_price_spread: float = 0.0,
) -> None:
```
`run()` 수정 — `_init_oi_history()` 호출 추가:
```python
async def run(self):
logger.info(f"봇 시작: {self.config.symbol}, 레버리지 {self.config.leverage}x")
await self._recover_position()
await self._init_oi_history()
...
```
**Step 5: Run tests**
Run: `bash scripts/run_tests.sh -k "test_bot"`
Expected: All PASS
**Step 6: Run full test suite**
Run: `bash scripts/run_tests.sh`
Expected: All PASS
**Step 7: Commit**
```bash
git add src/bot.py src/exchange.py tests/test_bot.py
git commit -m "feat: add OI history deque, cold start init, and derived features to bot runtime"
```
---
### Task 5: scripts/collect_oi.py — OI 장기 수집 스크립트
**Files:**
- Create: `scripts/collect_oi.py`
**Step 1: Implement**
```python
"""
OI 장기 수집 스크립트.
15분마다 cron 실행하여 Binance OI를 data/oi_history.parquet에 누적한다.
사용법:
python scripts/collect_oi.py
python scripts/collect_oi.py --symbol XRPUSDT
crontab 예시:
*/15 * * * * cd /path/to/cointrader && .venv/bin/python scripts/collect_oi.py >> logs/collect_oi.log 2>&1
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import argparse
from datetime import datetime, timezone
import pandas as pd
from binance.client import Client
from dotenv import load_dotenv
import os
load_dotenv()
OI_PATH = Path("data/oi_history.parquet")
def collect(symbol: str = "XRPUSDT"):
client = Client(
api_key=os.getenv("BINANCE_API_KEY", ""),
api_secret=os.getenv("BINANCE_API_SECRET", ""),
)
result = client.futures_open_interest(symbol=symbol)
oi_value = float(result["openInterest"])
ts = datetime.now(timezone.utc)
new_row = pd.DataFrame([{
"timestamp": ts,
"symbol": symbol,
"open_interest": oi_value,
}])
if OI_PATH.exists():
existing = pd.read_parquet(OI_PATH)
combined = pd.concat([existing, new_row], ignore_index=True)
else:
OI_PATH.parent.mkdir(parents=True, exist_ok=True)
combined = new_row
combined.to_parquet(OI_PATH, index=False)
print(f"[{ts.isoformat()}] OI={oi_value:.2f} → {OI_PATH}")
def main():
parser = argparse.ArgumentParser(description="OI 장기 수집")
parser.add_argument("--symbol", default="XRPUSDT")
args = parser.parse_args()
collect(symbol=args.symbol)
if __name__ == "__main__":
main()
```
**Step 2: Commit**
```bash
git add scripts/collect_oi.py
git commit -m "feat: add OI long-term collection script for cron-based data accumulation"
```
---
### Task 6: 기존 테스트 수정 및 전체 검증
**Files:**
- Modify: `tests/test_ml_features.py` (피처 수 변경)
- Modify: `tests/test_bot.py` (기존 OI 테스트가 4-tuple 반환에 호환되도록)
**Step 1: Fix test_ml_features.py assertions**
- `test_feature_cols_has_24_items` → 26으로 변경
- `test_build_features_with_btc_eth_has_24_features` → 26
- `test_build_features_without_btc_eth_has_16_features` → 18
**Step 2: Fix test_bot.py**
기존 `test_process_candle_fetches_oi_and_funding` 등에서 `_fetch_market_microstructure` 반환값이 4-tuple이 되므로 mock 반환값 수정:
```python
bot._fetch_market_microstructure = AsyncMock(return_value=(0.02, 0.0001, 0.015, 0.01))
```
또는 `_fetch_market_microstructure`를 mock하지 않는 테스트는 exchange mock이 정상이면 자동 통과.
**Step 3: Run full test suite**
Run: `bash scripts/run_tests.sh`
Expected: All PASS
**Step 4: Commit**
```bash
git add tests/test_ml_features.py tests/test_bot.py
git commit -m "test: update test assertions for 26-feature model and 4-tuple microstructure"
```
---
### Task 7: CLAUDE.md 업데이트
**Files:**
- Modify: `CLAUDE.md`
**Step 1: Update plan table**
CLAUDE.md의 plan history 테이블에 추가:
```
| 2026-03-04 | `oi-derived-features` (design + plan) | In Progress |
```
ml_features.py 설명도 24→26개로 갱신.
**Step 2: Commit**
```bash
git add CLAUDE.md
git commit -m "docs: update CLAUDE.md with OI derived features plan status"
```

64
scripts/collect_oi.py Normal file
View File

@@ -0,0 +1,64 @@
"""
OI 장기 수집 스크립트.
15분마다 cron 실행하여 Binance OI를 data/oi_history.parquet에 누적한다.
사용법:
python scripts/collect_oi.py
python scripts/collect_oi.py --symbol XRPUSDT
crontab 예시:
*/15 * * * * cd /path/to/cointrader && .venv/bin/python scripts/collect_oi.py >> logs/collect_oi.log 2>&1
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import argparse
from datetime import datetime, timezone
import pandas as pd
from binance.client import Client
from dotenv import load_dotenv
import os
load_dotenv()
OI_PATH = Path("data/oi_history.parquet")
def collect(symbol: str = "XRPUSDT"):
client = Client(
api_key=os.getenv("BINANCE_API_KEY", ""),
api_secret=os.getenv("BINANCE_API_SECRET", ""),
)
result = client.futures_open_interest(symbol=symbol)
oi_value = float(result["openInterest"])
ts = datetime.now(timezone.utc)
new_row = pd.DataFrame([{
"timestamp": ts,
"symbol": symbol,
"open_interest": oi_value,
}])
if OI_PATH.exists():
existing = pd.read_parquet(OI_PATH)
combined = pd.concat([existing, new_row], ignore_index=True)
else:
OI_PATH.parent.mkdir(parents=True, exist_ok=True)
combined = new_row
combined.to_parquet(OI_PATH, index=False)
print(f"[{ts.isoformat()}] OI={oi_value:.2f} → {OI_PATH}")
def main():
parser = argparse.ArgumentParser(description="OI 장기 수집")
parser.add_argument("--symbol", default="XRPUSDT")
args = parser.parse_args()
collect(symbol=args.symbol)
if __name__ == "__main__":
main()

113
scripts/train_model.py
View File

@@ -422,6 +422,113 @@ def walk_forward_auc(
print(f" 폴드별: {[round(a, 4) for a in aucs]}")
def compare(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None):
"""기존 피처 vs OI 파생 피처 추가 버전 A/B 비교."""
import warnings
print("=" * 70)
print(" OI 파생 피처 A/B 비교 (30일 데이터 기반, 방향성 참고용)")
print("=" * 70)
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()
if "oi_change" in df_raw.columns:
df["oi_change"] = df_raw["oi_change"]
if "funding_rate" in df_raw.columns:
df["funding_rate"] = df_raw["funding_rate"]
dataset = generate_dataset_vectorized(
df, btc_df=btc_df, eth_df=eth_df,
time_weight_decay=time_weight_decay,
negative_ratio=5,
)
if dataset.empty:
raise ValueError("데이터셋 생성 실패")
lgbm_params, weight_scale = _load_lgbm_params(tuned_params_path)
# Baseline: OI 파생 피처 제외
BASELINE_EXCLUDE = {"oi_change_ma5", "oi_price_spread"}
baseline_cols = [c for c in FEATURE_COLS if c in dataset.columns and c not in BASELINE_EXCLUDE]
new_cols = [c for c in FEATURE_COLS if c in dataset.columns]
results = {}
for label, cols in [("Baseline", baseline_cols), ("New", new_cols)]:
X = dataset[cols]
y = dataset["label"]
w = dataset["sample_weight"].values
source = dataset["source"].values if "source" in dataset.columns else np.full(len(X), "signal")
split = int(len(X) * 0.8)
X_tr, X_val = X.iloc[:split], X.iloc[split:]
y_tr, y_val = y.iloc[:split], y.iloc[split:]
w_tr = (w[:split] * weight_scale).astype(np.float32)
source_tr = source[:split]
balanced_idx = stratified_undersample(y_tr.values, source_tr, seed=42)
X_tr_b = X_tr.iloc[balanced_idx]
y_tr_b = y_tr.iloc[balanced_idx]
w_tr_b = w_tr[balanced_idx]
model = lgb.LGBMClassifier(**lgbm_params, random_state=42, verbose=-1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.fit(X_tr_b, y_tr_b, sample_weight=w_tr_b)
proba = model.predict_proba(X_val)[:, 1]
auc = roc_auc_score(y_val, proba) if len(np.unique(y_val)) > 1 else 0.5
precs, recs, thrs = precision_recall_curve(y_val, proba)
precs, recs = precs[:-1], recs[:-1]
valid_idx = np.where(recs >= 0.15)[0]
if len(valid_idx) > 0:
best_i = valid_idx[np.argmax(precs[valid_idx])]
thr, prec, rec = float(thrs[best_i]), float(precs[best_i]), float(recs[best_i])
else:
thr, prec, rec = 0.50, 0.0, 0.0
# Feature importance
imp = dict(zip(cols, model.feature_importances_))
top10 = sorted(imp.items(), key=lambda x: x[1], reverse=True)[:10]
results[label] = {
"auc": auc, "precision": prec, "recall": rec,
"threshold": thr, "n_val": len(y_val),
"n_val_pos": int(y_val.sum()), "top10": top10,
}
# 비교 테이블 출력
n_base = len(baseline_cols)
n_new = len(new_cols)
print(f"\n{'지표':<20} {f'Baseline({n_base})':>15} {f'New({n_new})':>15} {'Delta':>10}")
print("-" * 62)
for metric in ["auc", "precision", "recall", "threshold"]:
b = results["Baseline"][metric]
n = results["New"][metric]
d = n - b
sign = "+" if d > 0 else ""
print(f"{metric:<20} {b:>15.4f} {n:>15.4f} {sign}{d:>9.4f}")
n_val = results["Baseline"]["n_val"]
n_pos = results["Baseline"]["n_val_pos"]
print(f"\n검증셋: n={n_val} (양성={n_pos}, 음성={n_val - n_pos})")
print("⚠ 30일 데이터 기반 — 방향성 참고용\n")
print("Feature Importance Top 10 (New):")
for feat_name, imp_val in results["New"]["top10"]:
marker = " ← NEW" if feat_name in BASELINE_EXCLUDE else ""
print(f" {feat_name:<25} {imp_val:>6}{marker}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", default="data/combined_15m.parquet")
@@ -435,9 +542,13 @@ def main():
"--tuned-params", type=str, default=None,
help="Optuna 튜닝 결과 JSON 경로 (지정 시 기본 파라미터를 덮어씀)",
)
parser.add_argument("--compare", action="store_true",
help="OI 파생 피처 추가 전후 A/B 성능 비교")
args = parser.parse_args()
if args.wf:
if args.compare:
compare(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params)
elif args.wf:
walk_forward_auc(
args.data,
time_weight_decay=args.decay,

49
src/bot.py
View File

@@ -1,4 +1,5 @@
import asyncio
from collections import deque
import pandas as pd
from loguru import logger
from src.config import Config
@@ -24,6 +25,8 @@ class TradingBot:
self._entry_quantity: float | None = None
self._is_reentering: bool = False # _close_and_reenter 중 콜백 상태 초기화 방지
self._prev_oi: float | None = None # OI 변화율 계산용 이전 값
self._oi_history: deque = deque(maxlen=5)
self._latest_ret_1: float = 0.0
self.stream = MultiSymbolStream(
symbols=[config.symbol, "BTCUSDT", "ETHUSDT"],
interval="15m",
@@ -57,21 +60,43 @@ class TradingBot:
else:
logger.info("기존 포지션 없음 - 신규 진입 대기")
async def _fetch_market_microstructure(self) -> tuple[float, float]:
"""OI 변화율과 펀딩비를 실시간으로 조회한다. 실패 시 0.0으로 폴백."""
async def _init_oi_history(self) -> None:
"""봇 시작 시 최근 OI 변화율 히스토리를 조회하여 deque를 채운다."""
try:
changes = await self.exchange.get_oi_history(limit=5)
for c in changes:
self._oi_history.append(c)
if changes:
self._prev_oi = None
logger.info(f"OI 히스토리 초기화: {len(self._oi_history)}개")
except Exception as e:
logger.warning(f"OI 히스토리 초기화 실패 (무시): {e}")
async def _fetch_market_microstructure(self) -> tuple[float, float, float, float]:
"""OI 변화율, 펀딩비, OI MA5, OI-가격 스프레드를 실시간으로 조회한다."""
oi_val, fr_val = await asyncio.gather(
self.exchange.get_open_interest(),
self.exchange.get_funding_rate(),
return_exceptions=True,
)
# None(API 실패) 또는 Exception이면 _calc_oi_change를 호출하지 않고 0.0 반환
if isinstance(oi_val, (int, float)) and oi_val > 0:
oi_change = self._calc_oi_change(float(oi_val))
else:
oi_change = 0.0
fr_float = float(fr_val) if isinstance(fr_val, (int, float)) else 0.0
logger.debug(f"OI={oi_val}, OI변화율={oi_change:.6f}, 펀딩비={fr_float:.6f}")
return oi_change, fr_float
# OI 히스토리 업데이트 및 MA5 계산
self._oi_history.append(oi_change)
oi_ma5 = sum(self._oi_history) / len(self._oi_history) if self._oi_history else 0.0
# OI-가격 스프레드
oi_price_spread = oi_change - self._latest_ret_1
logger.debug(
f"OI={oi_val}, OI변화율={oi_change:.6f}, 펀딩비={fr_float:.6f}, "
f"OI_MA5={oi_ma5:.6f}, OI_Price_Spread={oi_price_spread:.6f}"
)
return oi_change, fr_float, oi_ma5, oi_price_spread
def _calc_oi_change(self, current_oi: float) -> float:
"""이전 OI 대비 변화율을 계산한다. 첫 캔들은 0.0 반환."""
@@ -85,8 +110,14 @@ class TradingBot:
async def process_candle(self, df, btc_df=None, eth_df=None):
self.ml_filter.check_and_reload()
# 가격 수익률 계산 (oi_price_spread용)
if len(df) >= 2:
prev_close = df["close"].iloc[-2]
curr_close = df["close"].iloc[-1]
self._latest_ret_1 = (curr_close - prev_close) / prev_close if prev_close != 0 else 0.0
# 캔들 마감 시 OI/펀딩비 실시간 조회 (실패해도 0으로 폴백)
oi_change, funding_rate = await self._fetch_market_microstructure()
oi_change, funding_rate, oi_ma5, oi_price_spread = await self._fetch_market_microstructure()
if not self.risk.is_trading_allowed():
logger.warning("리스크 한도 초과 - 거래 중단")
@@ -111,6 +142,7 @@ class TradingBot:
df_with_indicators, signal,
btc_df=btc_df, eth_df=eth_df,
oi_change=oi_change, funding_rate=funding_rate,
oi_change_ma5=oi_ma5, oi_price_spread=oi_price_spread,
)
if self.ml_filter.is_model_loaded():
if not self.ml_filter.should_enter(features):
@@ -126,6 +158,7 @@ class TradingBot:
position, raw_signal, df_with_indicators,
btc_df=btc_df, eth_df=eth_df,
oi_change=oi_change, funding_rate=funding_rate,
oi_change_ma5=oi_ma5, oi_price_spread=oi_price_spread,
)
async def _open_position(self, signal: str, df):
@@ -272,6 +305,8 @@ class TradingBot:
eth_df=None,
oi_change: float = 0.0,
funding_rate: float = 0.0,
oi_change_ma5: float = 0.0,
oi_price_spread: float = 0.0,
) -> None:
"""기존 포지션을 청산하고, ML 필터 통과 시 반대 방향으로 즉시 재진입한다."""
# 재진입 플래그: User Data Stream 콜백이 신규 포지션 상태를 초기화하지 않도록 보호
@@ -288,6 +323,7 @@ class TradingBot:
df, signal,
btc_df=btc_df, eth_df=eth_df,
oi_change=oi_change, funding_rate=funding_rate,
oi_change_ma5=oi_change_ma5, oi_price_spread=oi_price_spread,
)
if not self.ml_filter.should_enter(features):
logger.info(f"ML 필터 차단: {signal} 재진입 무시")
@@ -300,6 +336,7 @@ class TradingBot:
async def run(self):
logger.info(f"봇 시작: {self.config.symbol}, 레버리지 {self.config.leverage}x")
await self._recover_position()
await self._init_oi_history()
balance = await self.exchange.get_balance()
self.risk.set_base_balance(balance)
logger.info(f"기준 잔고 설정: {balance:.2f} USDT (동적 증거금 비율 기준점)")

16
src/dataset_builder.py
View File

@@ -287,8 +287,18 @@ def _calc_features_vectorized(
else:
fr_raw = np.full(len(d), np.nan)
result["oi_change"] = _rolling_zscore(oi_raw.astype(np.float64))
result["funding_rate"] = _rolling_zscore(fr_raw.astype(np.float64))
oi_z = _rolling_zscore(oi_raw.astype(np.float64), window=96)
result["oi_change"] = oi_z
result["funding_rate"] = _rolling_zscore(fr_raw.astype(np.float64), window=96)
# --- OI 파생 피처 ---
# 1. oi_change_ma5: OI 변화율의 5캔들 이동평균 (단기 추세)
oi_series = pd.Series(oi_raw.astype(np.float64))
oi_ma5_raw = oi_series.rolling(window=5, min_periods=1).mean().values
result["oi_change_ma5"] = _rolling_zscore(oi_ma5_raw, window=96)
# 2. oi_price_spread: z-scored OI - z-scored 가격 수익률 (연속값)
result["oi_price_spread"] = oi_z - ret_1_z
return result
@@ -384,7 +394,7 @@ def generate_dataset_vectorized(
feat_all = _calc_features_vectorized(d, signal_arr, btc_df=btc_df, eth_df=eth_df)
# 신호 발생 + NaN 없음 + 미래 데이터 충분한 인덱스만
OPTIONAL_COLS = {"oi_change", "funding_rate"}
OPTIONAL_COLS = {"oi_change", "funding_rate", "oi_change_ma5", "oi_price_spread"}
available_cols_for_nan_check = [
c for c in FEATURE_COLS
if c in feat_all.columns and c not in OPTIONAL_COLS

24
src/exchange.py
View File

@@ -173,6 +173,30 @@ class BinanceFuturesClient:
logger.warning(f"펀딩비 조회 실패 (무시): {e}")
return None
async def get_oi_history(self, limit: int = 5) -> list[float]:
"""최근 OI 변화율 히스토리를 조회한다 (봇 초기화용). 실패 시 빈 리스트."""
loop = asyncio.get_event_loop()
try:
result = await loop.run_in_executor(
None,
lambda: self.client.futures_open_interest_hist(
symbol=self.config.symbol, period="15m", limit=limit + 1,
),
)
if len(result) < 2:
return []
oi_values = [float(r["sumOpenInterest"]) for r in result]
changes = []
for i in range(1, len(oi_values)):
if oi_values[i - 1] > 0:
changes.append((oi_values[i] - oi_values[i - 1]) / oi_values[i - 1])
else:
changes.append(0.0)
return changes
except Exception as e:
logger.warning(f"OI 히스토리 조회 실패 (무시): {e}")
return []
async def create_listen_key(self) -> str:
"""POST /fapi/v1/listenKey — listenKey 신규 발급"""
loop = asyncio.get_event_loop()

15
src/ml_features.py
View File

@@ -9,8 +9,9 @@ FEATURE_COLS = [
"eth_ret_1", "eth_ret_3", "eth_ret_5",
"xrp_btc_rs", "xrp_eth_rs",
# 시장 미시구조: OI 변화율(z-score), 펀딩비(z-score)
# parquet에 oi_change/funding_rate 컬럼이 없으면 dataset_builder에서 0으로 채움
"oi_change", "funding_rate",
# OI 파생 피처
"oi_change_ma5", "oi_price_spread",
"adx",
]
@@ -37,12 +38,14 @@ def build_features(
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:
"""
기술 지표가 계산된 DataFrame의 마지막 행에서 ML 피처를 추출한다.
btc_df, eth_df가 제공되면 24개 피처를, 없으면 16개 피처를 반환한다.
btc_df, eth_df가 제공되면 26개 피처를, 없으면 18개 피처를 반환한다.
signal: "LONG" | "SHORT"
oi_change, funding_rate: 실제 값이 제공되면 사용, 없으면 0.0으로 채운다.
oi_change, funding_rate, oi_change_ma5, oi_price_spread: 실제 값이 제공되면 사용, 없으면 0.0으로 채운다.
"""
last = df.iloc[-1]
close = last["close"]
@@ -132,8 +135,10 @@ def build_features(
})
# 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"] = 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)

36
tests/test_bot.py
View File

@@ -227,6 +227,42 @@ async def test_process_candle_fetches_oi_and_funding(config, sample_df):
assert "funding_rate" in call_kwargs
def test_bot_has_oi_history_deque(config):
"""봇이 OI 히스토리 deque를 가져야 한다."""
from collections import deque
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
assert isinstance(bot._oi_history, deque)
assert bot._oi_history.maxlen == 5
@pytest.mark.asyncio
async def test_init_oi_history_fills_deque(config):
"""_init_oi_history가 deque를 채워야 한다."""
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
bot.exchange = AsyncMock()
bot.exchange.get_oi_history = AsyncMock(return_value=[0.01, -0.02, 0.03, -0.01, 0.02])
await bot._init_oi_history()
assert len(bot._oi_history) == 5
@pytest.mark.asyncio
async def test_fetch_microstructure_returns_4_tuple(config):
"""_fetch_market_microstructure가 4-tuple을 반환해야 한다."""
with patch("src.bot.BinanceFuturesClient"):
bot = TradingBot(config)
bot.exchange = AsyncMock()
bot.exchange.get_open_interest = AsyncMock(return_value=5000000.0)
bot.exchange.get_funding_rate = AsyncMock(return_value=0.0001)
bot._prev_oi = 4900000.0
bot._oi_history.extend([0.01, -0.02, 0.03, -0.01])
bot._latest_ret_1 = 0.01
result = await bot._fetch_market_microstructure()
assert len(result) == 4
def test_calc_oi_change_first_candle_returns_zero(config):
"""첫 캔들은 0.0을 반환하고 _prev_oi를 설정한다."""
with patch("src.bot.BinanceFuturesClient"):

71
tests/test_dataset_builder.py
View File

@@ -266,3 +266,74 @@ def test_stratified_undersample_preserves_signal():
signal_indices = np.where(source == "signal")[0]
for si in signal_indices:
assert si in idx, f"signal 인덱스 {si}가 누락됨"
def test_oi_derived_features_present():
"""OI 파생 피처 2개가 결과에 포함되어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 300
np.random.seed(42)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
"oi_change": np.concatenate([np.zeros(100), np.random.uniform(-0.05, 0.05, 200)]),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
assert "oi_change_ma5" in feat.columns, "oi_change_ma5 컬럼이 없음"
assert "oi_price_spread" in feat.columns, "oi_price_spread 컬럼이 없음"
def test_oi_derived_features_nan_when_no_oi():
"""oi_change 컬럼이 없으면 파생 피처도 nan이어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 200
np.random.seed(0)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
assert feat["oi_change_ma5"].isna().all(), "oi_change 컬럼 없을 때 oi_change_ma5는 전부 nan이어야 함"
assert feat["oi_price_spread"].isna().all(), "oi_change 컬럼 없을 때 oi_price_spread는 전부 nan이어야 함"
def test_oi_price_spread_is_continuous():
"""oi_price_spread는 바이너리가 아닌 연속값이어야 한다."""
import numpy as np
import pandas as pd
from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
n = 300
np.random.seed(42)
df = pd.DataFrame({
"open": np.random.uniform(1, 2, n),
"high": np.random.uniform(2, 3, n),
"low": np.random.uniform(0.5, 1, n),
"close": np.random.uniform(1, 2, n),
"volume": np.random.uniform(1000, 5000, n),
"oi_change": np.random.uniform(-0.05, 0.05, n),
})
d = _calc_indicators(df)
sig = _calc_signals(d)
feat = _calc_features_vectorized(d, sig)
valid = feat["oi_price_spread"].dropna()
assert len(valid.unique()) > 2, "oi_price_spread는 연속값이어야 함 (2개 초과 유니크 값)"

40
tests/test_exchange.py
View File

@@ -113,3 +113,43 @@ async def test_get_funding_rate_error_returns_none(exchange):
)
result = await exchange.get_funding_rate()
assert result is None
@pytest.mark.asyncio
async def test_get_oi_history_returns_changes(exchange):
"""get_oi_history()가 OI 변화율 리스트를 반환하는지 확인."""
exchange.client.futures_open_interest_hist = MagicMock(
return_value=[
{"sumOpenInterest": "1000000"},
{"sumOpenInterest": "1010000"},
{"sumOpenInterest": "1005000"},
{"sumOpenInterest": "1020000"},
{"sumOpenInterest": "1015000"},
{"sumOpenInterest": "1030000"},
]
)
result = await exchange.get_oi_history(limit=5)
assert len(result) == 5
assert isinstance(result[0], float)
# 첫 번째 변화율: (1010000 - 1000000) / 1000000 = 0.01
assert abs(result[0] - 0.01) < 1e-6
@pytest.mark.asyncio
async def test_get_oi_history_error_returns_empty(exchange):
"""API 오류 시 빈 리스트 반환 확인."""
exchange.client.futures_open_interest_hist = MagicMock(
side_effect=Exception("API error")
)
result = await exchange.get_oi_history(limit=5)
assert result == []
@pytest.mark.asyncio
async def test_get_oi_history_insufficient_data_returns_empty(exchange):
"""데이터가 부족하면 빈 리스트 반환 확인."""
exchange.client.futures_open_interest_hist = MagicMock(
return_value=[{"sumOpenInterest": "1000000"}]
)
result = await exchange.get_oi_history(limit=5)
assert result == []

39
tests/test_ml_features.py
View File

@@ -21,17 +21,17 @@ def _make_df(n=10, base_price=1.0):
})
def test_build_features_with_btc_eth_has_24_features():
def test_build_features_with_btc_eth_has_26_features():
xrp_df = _make_df(10, base_price=1.0)
btc_df = _make_df(10, base_price=50000.0)
eth_df = _make_df(10, base_price=3000.0)
features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
assert len(features) == 24
assert len(features) == 26
def test_build_features_without_btc_eth_has_16_features():
def test_build_features_without_btc_eth_has_18_features():
xrp_df = _make_df(10, base_price=1.0)
features = build_features(xrp_df, "LONG")
assert len(features) == 16
assert len(features) == 18
def test_build_features_btc_ret_1_correct():
xrp_df = _make_df(10, base_price=1.0)
@@ -51,8 +51,9 @@ def test_build_features_rs_zero_when_btc_ret_zero():
assert features["xrp_btc_rs"] == 0.0
def test_feature_cols_has_24_items():
"""Legacy test — updated to 26 after OI derived features added."""
from src.ml_features import FEATURE_COLS
assert len(FEATURE_COLS) == 24
assert len(FEATURE_COLS) == 26
def make_df(n=100):
@@ -139,3 +140,31 @@ def test_build_features_defaults_to_zero_when_not_provided(sample_df_with_indica
feat = build_features(sample_df_with_indicators, signal="LONG")
assert feat["oi_change"] == pytest.approx(0.0)
assert feat["funding_rate"] == pytest.approx(0.0)
def test_feature_cols_has_26_items():
from src.ml_features import FEATURE_COLS
assert len(FEATURE_COLS) == 26
def test_build_features_with_oi_derived_params():
"""oi_change_ma5, oi_price_spread 파라미터가 피처에 반영된다."""
xrp_df = _make_df(10, base_price=1.0)
btc_df = _make_df(10, base_price=50000.0)
eth_df = _make_df(10, base_price=3000.0)
features = build_features(
xrp_df, "LONG",
btc_df=btc_df, eth_df=eth_df,
oi_change=0.05, funding_rate=0.0002,
oi_change_ma5=0.03, oi_price_spread=0.12,
)
assert features["oi_change_ma5"] == pytest.approx(0.03)
assert features["oi_price_spread"] == pytest.approx(0.12)
def test_build_features_oi_derived_defaults_to_zero():
"""oi_change_ma5, oi_price_spread 미제공 시 0.0으로 채워진다."""
xrp_df = _make_df(10, base_price=1.0)
features = build_features(xrp_df, "LONG")
assert features["oi_change_ma5"] == pytest.approx(0.0)
assert features["oi_price_spread"] == pytest.approx(0.0)