d1af736bfc
- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy. - Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH. - Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features. - Updated various scripts and modules to support the new feature set and data handling. - Enhanced training and deployment scripts to accommodate the new dataset structure. This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from src.dataset_builder import generate_dataset_vectorized
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@pytest.fixture
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def sample_df():
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"""최소 200행 이상의 OHLCV 더미 데이터."""
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rng = np.random.default_rng(42)
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n = 500
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close = 2.0 + np.cumsum(rng.normal(0, 0.01, n))
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close = np.clip(close, 0.01, None)
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high = close * (1 + rng.uniform(0, 0.005, n))
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low = close * (1 - rng.uniform(0, 0.005, n))
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return pd.DataFrame({
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"open": close,
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"high": high,
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"low": low,
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"close": close,
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"volume": rng.uniform(1e6, 5e6, n),
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})
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def test_returns_dataframe(sample_df):
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"""결과가 DataFrame이어야 한다."""
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result = generate_dataset_vectorized(sample_df)
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assert isinstance(result, pd.DataFrame)
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def test_has_required_columns(sample_df):
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"""기본 13개 피처 + label 컬럼이 모두 있어야 한다."""
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BASE_FEATURE_COLS = [
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"rsi", "macd_hist", "bb_pct", "ema_align",
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"stoch_k", "stoch_d", "atr_pct", "vol_ratio",
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"ret_1", "ret_3", "ret_5", "signal_strength", "side",
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]
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result = generate_dataset_vectorized(sample_df)
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if len(result) > 0:
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assert "label" in result.columns
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for col in BASE_FEATURE_COLS:
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assert col in result.columns, f"컬럼 없음: {col}"
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def test_label_is_binary(sample_df):
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"""label은 0 또는 1만 있어야 한다."""
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result = generate_dataset_vectorized(sample_df)
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if len(result) > 0:
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assert set(result["label"].unique()).issubset({0, 1})
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def test_generate_dataset_vectorized_with_btc_eth_has_21_feature_cols():
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"""BTC/ETH DataFrame을 전달하면 결과 컬럼이 21개 피처 + label이어야 한다."""
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import pandas as pd
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import numpy as np
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from src.dataset_builder import generate_dataset_vectorized
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from src.ml_features import FEATURE_COLS
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np.random.seed(42)
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n = 500
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closes = np.cumprod(1 + np.random.randn(n) * 0.001) * 1.0
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xrp_df = pd.DataFrame({
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"open": closes * 0.999, "high": closes * 1.005,
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"low": closes * 0.995, "close": closes,
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"volume": np.random.rand(n) * 1000 + 500,
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})
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btc_df = xrp_df.copy() * 50000
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eth_df = xrp_df.copy() * 3000
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result = generate_dataset_vectorized(xrp_df, btc_df=btc_df, eth_df=eth_df)
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if not result.empty:
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assert set(FEATURE_COLS).issubset(set(result.columns))
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assert len(result.columns) == len(FEATURE_COLS) + 1 # +1 for label
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def test_matches_original_generate_dataset(sample_df):
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"""벡터화 버전과 기존 버전의 샘플 수가 유사해야 한다.
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벡터화 버전은 전체 시계열로 지표를 1회 계산하고, 기존 버전은 61행 슬라이딩
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윈도우로 매번 재계산한다. EMA 등 지수 이동평균은 초기값에 따라 수렴 속도가
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달라지므로 두 방식의 신호 수는 완전히 동일하지 않을 수 있다. ±50% 범위를
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허용한다.
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"""
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from scripts.train_model import generate_dataset
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orig = generate_dataset(sample_df, n_jobs=1)
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vec = generate_dataset_vectorized(sample_df)
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if len(orig) == 0:
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assert len(vec) == 0
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return
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ratio = len(vec) / len(orig)
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assert 0.5 <= ratio <= 2.0, (
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f"샘플 수 차이가 너무 큼: 벡터화={len(vec)}, 기존={len(orig)}, 비율={ratio:.2f}"
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)
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