684c8a32b9
- Introduced support for Algo Order API, allowing automatic sending of STOP_MARKET and TAKE_PROFIT_MARKET orders. - Updated README.md to include new features related to Algo Order API and real-time handling of ML features. - Enhanced ML feature processing to fill missing OI and funding rate values with zeros for consistency in training data. - Added new training log entries for the lgbm model with updated metrics.
135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
import pandas as pd
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import numpy as np
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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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"btc_ret_1", "btc_ret_3", "btc_ret_5",
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"eth_ret_1", "eth_ret_3", "eth_ret_5",
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"xrp_btc_rs", "xrp_eth_rs",
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# 시장 미시구조: OI 변화율(z-score), 펀딩비(z-score)
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# parquet에 oi_change/funding_rate 컬럼이 없으면 dataset_builder에서 0으로 채움
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"oi_change", "funding_rate",
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]
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def _calc_ret(closes: pd.Series, n: int) -> float:
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"""n캔들 전 대비 수익률. 데이터 부족 시 0.0."""
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if len(closes) < n + 1:
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return 0.0
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prev = closes.iloc[-(n + 1)]
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return (closes.iloc[-1] - prev) / prev if prev != 0 else 0.0
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def _calc_rs(xrp_ret: float, other_ret: float) -> float:
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"""상대강도 = xrp_ret / other_ret. 분모 0이면 0.0."""
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if other_ret == 0.0:
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return 0.0
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return xrp_ret / other_ret
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def build_features(
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df: pd.DataFrame,
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signal: str,
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btc_df: pd.DataFrame | None = None,
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eth_df: pd.DataFrame | None = None,
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) -> pd.Series:
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"""
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기술 지표가 계산된 DataFrame의 마지막 행에서 ML 피처를 추출한다.
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btc_df, eth_df가 제공되면 21개 피처를, 없으면 13개 피처를 반환한다.
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signal: "LONG" | "SHORT"
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"""
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last = df.iloc[-1]
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close = last["close"]
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bb_upper = last.get("bb_upper", close)
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bb_lower = last.get("bb_lower", close)
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bb_range = bb_upper - bb_lower
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bb_pct = (close - bb_lower) / bb_range if bb_range > 0 else 0.5
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ema9 = last.get("ema9", close)
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ema21 = last.get("ema21", close)
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ema50 = last.get("ema50", close)
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if ema9 > ema21 > ema50:
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ema_align = 1
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elif ema9 < ema21 < ema50:
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ema_align = -1
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else:
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ema_align = 0
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atr = last.get("atr", 0)
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atr_pct = atr / close if close > 0 else 0
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vol_ma20 = last.get("vol_ma20", last.get("volume", 1))
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vol_ratio = last["volume"] / vol_ma20 if vol_ma20 > 0 else 1.0
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closes = df["close"]
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ret_1 = _calc_ret(closes, 1)
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ret_3 = _calc_ret(closes, 3)
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ret_5 = _calc_ret(closes, 5)
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prev = df.iloc[-2] if len(df) >= 2 else last
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strength = 0
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rsi = last.get("rsi", 50)
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macd = last.get("macd", 0)
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macd_sig = last.get("macd_signal", 0)
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prev_macd = prev.get("macd", 0)
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prev_macd_sig = prev.get("macd_signal", 0)
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stoch_k = last.get("stoch_k", 50)
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stoch_d = last.get("stoch_d", 50)
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if signal == "LONG":
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if rsi < 35: strength += 1
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if prev_macd < prev_macd_sig and macd > macd_sig: strength += 2
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if close < last.get("bb_lower", close): strength += 1
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if ema_align == 1: strength += 1
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if stoch_k < 20 and stoch_k > stoch_d: strength += 1
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else:
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if rsi > 65: strength += 1
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if prev_macd > prev_macd_sig and macd < macd_sig: strength += 2
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if close > last.get("bb_upper", close): strength += 1
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if ema_align == -1: strength += 1
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if stoch_k > 80 and stoch_k < stoch_d: strength += 1
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base = {
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"rsi": float(rsi),
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"macd_hist": float(last.get("macd_hist", 0)),
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"bb_pct": float(bb_pct),
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"ema_align": float(ema_align),
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"stoch_k": float(stoch_k),
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"stoch_d": float(last.get("stoch_d", 50)),
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"atr_pct": float(atr_pct),
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"vol_ratio": float(vol_ratio),
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"ret_1": float(ret_1),
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"ret_3": float(ret_3),
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"ret_5": float(ret_5),
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"signal_strength": float(strength),
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"side": 1.0 if signal == "LONG" else 0.0,
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}
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if btc_df is not None and eth_df is not None:
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btc_ret_1 = _calc_ret(btc_df["close"], 1)
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btc_ret_3 = _calc_ret(btc_df["close"], 3)
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btc_ret_5 = _calc_ret(btc_df["close"], 5)
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eth_ret_1 = _calc_ret(eth_df["close"], 1)
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eth_ret_3 = _calc_ret(eth_df["close"], 3)
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eth_ret_5 = _calc_ret(eth_df["close"], 5)
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base.update({
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"btc_ret_1": float(btc_ret_1),
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"btc_ret_3": float(btc_ret_3),
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"btc_ret_5": float(btc_ret_5),
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"eth_ret_1": float(eth_ret_1),
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"eth_ret_3": float(eth_ret_3),
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"eth_ret_5": float(eth_ret_5),
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"xrp_btc_rs": float(_calc_rs(ret_1, btc_ret_1)),
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"xrp_eth_rs": float(_calc_rs(ret_1, eth_ret_1)),
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})
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# 실시간에서는 OI/펀딩비를 수집하지 않으므로 0으로 채워 학습 피처(23개)와 일치시킨다
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base.setdefault("oi_change", 0.0)
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base.setdefault("funding_rate", 0.0)
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return pd.Series(base)
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