feat: add testnet mode support and fix UDS order type classification
- Add BINANCE_TESTNET env var to switch between live/demo API keys - Add KLINE_INTERVAL env var (default 15m) for configurable candle interval - Pass testnet flag through to Exchange, DataStream, UDS, Notifier - Add demo mode in bot: forced LONG entry with fixed 0.5% SL / 2% TP - Fix UDS close_reason: use ot (original order type) field to correctly classify STOP_MARKET/TAKE_PROFIT_MARKET triggers (was MANUAL) - Add UDS raw event logging with ot field for debugging - Add backtest market context (BTC/ETH regime, L/S ratio per fold) - Separate testnet trade history to data/trade_history/testnet/ Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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21in7
@@ -20,6 +20,8 @@ import json
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from datetime import datetime
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import numpy as np
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import pandas as pd
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import pandas_ta as ta
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from loguru import logger
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@@ -107,6 +109,174 @@ def print_fold_table(folds: list[dict]):
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print("=" * 90)
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def _classify_regime(btc_return: float, btc_avg_adx: float) -> str:
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"""BTC ADX와 수익률 기반 시장 레짐 분류."""
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if btc_avg_adx >= 25:
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return "상승 추세" if btc_return > 0 else "하락 추세"
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return "횡보"
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def _calc_fold_market_context(
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raw_df: pd.DataFrame, test_start: str, test_end: str
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) -> dict:
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"""폴드 기간의 BTC/ETH 수익률과 시장 레짐 계산."""
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ts_start = pd.Timestamp(test_start)
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ts_end = pd.Timestamp(test_end)
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idx = raw_df.index
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if idx.tz is not None:
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idx = idx.tz_localize(None)
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if ts_start.tz is not None:
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ts_start = ts_start.tz_localize(None)
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if ts_end.tz is not None:
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ts_end = ts_end.tz_localize(None)
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fold_df = raw_df[(idx >= ts_start) & (idx < ts_end)]
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if len(fold_df) < 20:
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return None
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# BTC return
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btc_start = fold_df["close_btc"].iloc[0]
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btc_end = fold_df["close_btc"].iloc[-1]
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btc_return = (btc_end - btc_start) / btc_start * 100
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# ETH return
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eth_start = fold_df["close_eth"].iloc[0]
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eth_end = fold_df["close_eth"].iloc[-1]
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eth_return = (eth_end - eth_start) / eth_start * 100
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# BTC ADX (period average)
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adx_df = ta.adx(fold_df["high_btc"], fold_df["low_btc"], fold_df["close_btc"], length=14)
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btc_avg_adx = adx_df["ADX_14"].mean()
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if np.isnan(btc_avg_adx):
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btc_avg_adx = 0.0
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regime = _classify_regime(btc_return, btc_avg_adx)
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return {
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"btc_return_pct": round(btc_return, 1),
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"eth_return_pct": round(eth_return, 1),
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"btc_avg_adx": round(btc_avg_adx, 1),
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"market_regime": regime,
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}
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def _load_ls_ratio(symbol: str, test_start: str, test_end: str) -> dict | None:
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"""폴드 기간의 L/S ratio 평균값 로드. 데이터 없으면 None."""
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path = Path(f"data/{symbol.lower()}/ls_ratio_15m.parquet")
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if not path.exists():
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return None
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df = pd.read_parquet(path)
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ts_start = pd.Timestamp(test_start)
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ts_end = pd.Timestamp(test_end)
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# tz 맞추기
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if df["timestamp"].dt.tz is not None:
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if ts_start.tz is None:
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ts_start = ts_start.tz_localize("UTC")
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if ts_end.tz is None:
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ts_end = ts_end.tz_localize("UTC")
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mask = (df["timestamp"] >= ts_start) & (df["timestamp"] < ts_end)
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period_df = df[mask]
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if period_df.empty:
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return None
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return {
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"top_acct_avg": round(period_df["top_acct_ls_ratio"].mean(), 2),
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"global_avg": round(period_df["global_ls_ratio"].mean(), 2),
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}
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def calc_market_context(folds: list[dict], symbols: list[str]) -> list[dict]:
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"""각 폴드에 대한 시장 컨텍스트 계산."""
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# XRP parquet에서 BTC/ETH 데이터 로드 (임베딩됨)
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primary_sym = symbols[0].lower()
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raw_path = Path(f"data/{primary_sym}/combined_15m.parquet")
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if not raw_path.exists():
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logger.warning(f"데이터 파일 없음: {raw_path}")
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return []
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raw_df = pd.read_parquet(raw_path)
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if "close_btc" not in raw_df.columns or "close_eth" not in raw_df.columns:
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logger.warning("BTC/ETH 상관 데이터 없음")
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return []
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contexts = []
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for fold in folds:
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test_start = fold.get("test_start")
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test_end = fold.get("test_end")
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if not test_start or not test_end:
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contexts.append({"fold": fold["fold"], "market_context": None})
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continue
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ctx = _calc_fold_market_context(raw_df, test_start, test_end)
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if ctx is None:
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contexts.append({"fold": fold["fold"], "market_context": None})
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continue
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# L/S ratio (XRP, BTC, ETH)
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ls_data = {}
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for ls_sym in ["xrpusdt", "btcusdt", "ethusdt"]:
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ls = _load_ls_ratio(ls_sym, test_start, test_end)
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if ls:
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ls_data[ls_sym.replace("usdt", "")] = ls
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ctx["ls_ratio"] = ls_data if ls_data else None
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contexts.append({"fold": fold["fold"], "market_context": ctx})
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return contexts
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def print_market_context(contexts: list[dict]):
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"""시장 컨텍스트 테이블 출력."""
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if not contexts:
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return
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# Market Regime 테이블
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print("\n📊 Market Context per Fold")
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print(f"{'─' * 80}")
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print(f" {'Fold':>4} {'BTC Return':>12} {'ETH Return':>12} {'Market Regime':<32}")
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print(f"{'─' * 80}")
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for c in contexts:
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ctx = c.get("market_context")
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if ctx is None:
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print(f" {c['fold']:>4} {'N/A':>12} {'N/A':>12} {'N/A':<32}")
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else:
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regime_str = f"{ctx['market_regime']} (BTC ADX {ctx['btc_avg_adx']:.0f})"
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print(f" {c['fold']:>4} {ctx['btc_return_pct']:>+11.1f}% "
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f"{ctx['eth_return_pct']:>+11.1f}% {regime_str:<32}")
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print(f"{'─' * 80}")
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# L/S Ratio 테이블 (데이터 있는 폴드가 하나라도 있으면)
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has_ls = any(
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c.get("market_context") and c["market_context"].get("ls_ratio")
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for c in contexts
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)
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if has_ls:
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print("\n📊 L/S Ratio Context per Fold (period avg)")
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print(f"{'─' * 80}")
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print(f" {'Fold':>4} {'XRP Top/Global':>18} {'BTC Top/Global':>18} {'ETH Top/Global':>18}")
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print(f"{'─' * 80}")
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for c in contexts:
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ctx = c.get("market_context")
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ls = ctx.get("ls_ratio") if ctx else None
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parts = []
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for sym in ["xrp", "btc", "eth"]:
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if ls and sym in ls:
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parts.append(f"{ls[sym]['top_acct_avg']:.2f} / {ls[sym]['global_avg']:.2f}")
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else:
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parts.append("N/A")
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print(f" {c['fold']:>4} {parts[0]:>18} {parts[1]:>18} {parts[2]:>18}")
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print(f"{'─' * 80}")
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else:
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print(" ℹ️ L/S ratio 데이터 없음 — collector 데이터 축적 후 표시됩니다")
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def save_result(result: dict, cfg):
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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mode = result.get("mode", "standard")
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@@ -183,6 +353,11 @@ def compare_ml(symbols: list[str], args):
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print_summary(result["summary"], cfg, mode="walk_forward")
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if result.get("folds"):
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print_fold_table(result["folds"])
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# 시장 컨텍스트는 첫 번째 실행에서만 출력 (동일 데이터)
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if label == "ML OFF":
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contexts = calc_market_context(result["folds"], symbols)
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if contexts:
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print_market_context(contexts)
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_print_comparison(results, symbols)
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@@ -343,6 +518,12 @@ def main():
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print_summary(result["summary"], cfg, mode="walk_forward")
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if result.get("folds"):
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print_fold_table(result["folds"])
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contexts = calc_market_context(result["folds"], symbols)
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if contexts:
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print_market_context(contexts)
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# JSON에 market_context 추가
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for fold, ctx in zip(result["folds"], contexts):
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fold["market_context"] = ctx.get("market_context")
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save_result(result, cfg)
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else:
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cfg = BacktestConfig(
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