feat: add L/S ratio collector service for top_acct and global ratios
Collect top trader account L/S ratio and global L/S ratio every 15 minutes for XRP, BTC, ETH (6 API calls/cycle) and persist to per-symbol parquet files. Deployed as a separate Docker service reusing the bot image. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
21in7
7
Jenkinsfile
vendored
7
Jenkinsfile
vendored
@@ -50,7 +50,7 @@ pipeline {
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env.DASH_API_CHANGED = 'true'
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env.DASH_UI_CHANGED = 'true'
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} else {
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env.BOT_CHANGED = (changes =~ /(?m)^(src\/|main\.py|requirements\.txt|Dockerfile)/).find() ? 'true' : 'false'
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env.BOT_CHANGED = (changes =~ /(?m)^(src\/|scripts\/|main\.py|requirements\.txt|Dockerfile)/).find() ? 'true' : 'false'
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env.DASH_API_CHANGED = (changes =~ /(?m)^dashboard\/api\//).find() ? 'true' : 'false'
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env.DASH_UI_CHANGED = (changes =~ /(?m)^dashboard\/ui\//).find() ? 'true' : 'false'
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}
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@@ -123,7 +123,10 @@ pipeline {
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// 변경된 서비스만 pull & recreate (나머지는 중단 없음)
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def services = []
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if (env.BOT_CHANGED == 'true') services.add('cointrader')
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if (env.BOT_CHANGED == 'true') {
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services.add('cointrader')
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services.add('ls-ratio-collector')
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}
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if (env.DASH_API_CHANGED == 'true') services.add('dashboard-api')
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if (env.DASH_UI_CHANGED == 'true') services.add('dashboard-ui')
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@@ -52,5 +52,21 @@ services:
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max-size: "10m"
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max-file: "3"
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ls-ratio-collector:
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image: git.gihyeon.com/gihyeon/cointrader:latest
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container_name: ls-ratio-collector
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restart: unless-stopped
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environment:
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- TZ=Asia/Seoul
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- PYTHONUNBUFFERED=1
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volumes:
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- ./data:/app/data
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entrypoint: ["sh", "scripts/collect_ls_ratio_loop.sh"]
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logging:
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driver: "json-file"
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options:
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max-size: "5m"
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max-file: "3"
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volumes:
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dashboard-data:
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121
scripts/collect_ls_ratio.py
Normal file
121
scripts/collect_ls_ratio.py
Normal file
@@ -0,0 +1,121 @@
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"""
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Long/Short Ratio 장기 수집 스크립트.
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15분마다 cron 실행하여 Binance Trading Data API에서
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top_acct_ls_ratio, global_ls_ratio를 data/{symbol}/ls_ratio_15m.parquet에 누적한다.
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수집 대상:
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- topLongShortAccountRatio × 3심볼 (XRPUSDT, BTCUSDT, ETHUSDT)
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- globalLongShortAccountRatio × 3심볼 (XRPUSDT, BTCUSDT, ETHUSDT)
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→ 총 API 호출 6회/15분 (rate limit 무관)
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사용법:
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python scripts/collect_ls_ratio.py
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python scripts/collect_ls_ratio.py --symbols XRPUSDT BTCUSDT
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import argparse
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import asyncio
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from datetime import datetime, timezone
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import aiohttp
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import pandas as pd
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BASE_URL = "https://fapi.binance.com"
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DEFAULT_SYMBOLS = ["XRPUSDT", "BTCUSDT", "ETHUSDT"]
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ENDPOINTS = {
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"top_acct_ls_ratio": "/futures/data/topLongShortAccountRatio",
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"global_ls_ratio": "/futures/data/globalLongShortAccountRatio",
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}
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async def fetch_latest(session: aiohttp.ClientSession, symbol: str) -> dict | None:
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"""심볼 하나에 대해 두 ratio의 최신 1건씩 가져온다."""
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row = {"timestamp": None, "symbol": symbol}
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for col_name, endpoint in ENDPOINTS.items():
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url = f"{BASE_URL}{endpoint}"
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params = {"symbol": symbol, "period": "15m", "limit": 1}
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try:
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async with session.get(url, params=params, timeout=aiohttp.ClientTimeout(total=10)) as resp:
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data = await resp.json()
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if isinstance(data, list) and data:
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row[col_name] = float(data[0]["longShortRatio"])
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# 타임스탬프는 첫 번째 응답에서 설정
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if row["timestamp"] is None:
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row["timestamp"] = pd.Timestamp(
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int(data[0]["timestamp"]), unit="ms", tz="UTC"
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)
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else:
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print(f"[WARN] {symbol} {col_name}: unexpected response: {data}")
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return None
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except Exception as e:
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print(f"[ERROR] {symbol} {col_name}: {e}")
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return None
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return row
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async def collect(symbols: list[str]):
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"""모든 심볼 데이터를 수집하고 parquet에 추가한다."""
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async with aiohttp.ClientSession() as session:
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tasks = [fetch_latest(session, sym) for sym in symbols]
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results = await asyncio.gather(*tasks)
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now = datetime.now(timezone.utc)
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collected = 0
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for row in results:
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if row is None:
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continue
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symbol = row["symbol"]
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out_path = Path(f"data/{symbol.lower()}/ls_ratio_15m.parquet")
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out_path.parent.mkdir(parents=True, exist_ok=True)
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new_df = pd.DataFrame([{
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"timestamp": row["timestamp"],
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"top_acct_ls_ratio": row["top_acct_ls_ratio"],
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"global_ls_ratio": row["global_ls_ratio"],
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}])
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if out_path.exists():
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existing = pd.read_parquet(out_path)
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# 중복 방지: 동일 timestamp가 이미 있으면 스킵
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if row["timestamp"] in existing["timestamp"].values:
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print(f"[SKIP] {symbol} ts={row['timestamp']} already exists")
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continue
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combined = pd.concat([existing, new_df], ignore_index=True)
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else:
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combined = new_df
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combined.to_parquet(out_path, index=False)
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collected += 1
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print(
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f"[{now.isoformat()}] {symbol}: "
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f"top_acct={row['top_acct_ls_ratio']:.4f}, "
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f"global={row['global_ls_ratio']:.4f} "
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f"→ {out_path} ({len(combined)} rows)"
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)
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if collected == 0:
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print(f"[{now.isoformat()}] No new data collected")
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def main():
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parser = argparse.ArgumentParser(description="L/S Ratio 장기 수집")
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parser.add_argument(
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"--symbols", nargs="+", default=DEFAULT_SYMBOLS,
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help="수집 대상 심볼 (기본: XRPUSDT BTCUSDT ETHUSDT)",
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)
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args = parser.parse_args()
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asyncio.run(collect(args.symbols))
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if __name__ == "__main__":
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main()
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27
scripts/collect_ls_ratio_loop.sh
Executable file
27
scripts/collect_ls_ratio_loop.sh
Executable file
@@ -0,0 +1,27 @@
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#!/bin/sh
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# 15분 경계에 맞춰 collect_ls_ratio.py를 반복 실행한다.
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# Docker 컨테이너 entrypoint용.
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set -e
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echo "[collect_ls_ratio] Starting loop (interval: 15m)"
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while true; do
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# 현재 분/초를 기준으로 다음 15분 경계(00/15/30/45)까지 대기
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now_min=$(date -u +%M | sed 's/^0//')
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now_sec=$(date -u +%S | sed 's/^0//')
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# 다음 15분 경계까지 남은 분
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remainder=$((now_min % 15))
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wait_min=$((15 - remainder))
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# 초 단위로 변환 (경계 직후 10초 여유)
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wait_sec=$(( wait_min * 60 - now_sec + 10 ))
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if [ "$wait_sec" -le 10 ]; then
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wait_sec=$((wait_sec + 900))
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fi
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echo "[collect_ls_ratio] Next run in ${wait_sec}s ($(date -u))"
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sleep "$wait_sec"
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echo "[collect_ls_ratio] Running collection... ($(date -u))"
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python scripts/collect_ls_ratio.py || echo "[collect_ls_ratio] ERROR: collection failed"
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done
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256
scripts/taker_ratio_analysis.py
Normal file
256
scripts/taker_ratio_analysis.py
Normal file
@@ -0,0 +1,256 @@
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"""
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Taker Buy/Sell Ratio vs Next-Candle Price Change Correlation Analysis
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- Taker Buy Ratio (from klines + Trading Data API)
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- Long/Short Ratio (global)
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- Top Trader Long/Short Ratio (accounts & positions)
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Usage: python scripts/taker_ratio_analysis.py [SYMBOL1] [SYMBOL2] ...
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Default: XRPUSDT BTCUSDT ETHUSDT
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"""
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import asyncio
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import aiohttp
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta, timezone
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import sys
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BASE = "https://fapi.binance.com"
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SYMBOLS = sys.argv[1:] if len(sys.argv) > 1 else ["XRPUSDT", "BTCUSDT", "ETHUSDT"]
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INTERVAL = "15m"
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DAYS = 30
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async def fetch_json(session, url, params):
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async with session.get(url, params=params) as resp:
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return await resp.json()
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async def fetch_klines(session, symbol, start_ms, end_ms):
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all_klines = []
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current = start_ms
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while current < end_ms:
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params = {"symbol": symbol, "interval": INTERVAL, "startTime": current, "endTime": end_ms, "limit": 1500}
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data = await fetch_json(session, f"{BASE}/fapi/v1/klines", params)
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if not data:
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break
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all_klines.extend(data)
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current = data[-1][0] + 1
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return all_klines
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async def fetch_ratio(session, url, symbol):
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params = {"symbol": symbol, "period": INTERVAL, "limit": 500}
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data = await fetch_json(session, url, params)
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return data if isinstance(data, list) else []
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async def analyze_symbol(session, symbol, start_ms, end_ms):
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"""Fetch and analyze a single symbol"""
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klines, ls_ratio, top_acct, top_pos, taker = await asyncio.gather(
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fetch_klines(session, symbol, start_ms, end_ms),
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fetch_ratio(session, f"{BASE}/futures/data/globalLongShortAccountRatio", symbol),
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fetch_ratio(session, f"{BASE}/futures/data/topLongShortAccountRatio", symbol),
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fetch_ratio(session, f"{BASE}/futures/data/topLongShortPositionRatio", symbol),
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fetch_ratio(session, f"{BASE}/futures/data/takerlongshortRatio", symbol),
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)
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print(f"\n {symbol}: Klines={len(klines)}, L/S={len(ls_ratio)}, TopAcct={len(top_acct)}, TopPos={len(top_pos)}, Taker={len(taker)}")
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# Build DataFrame
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df_k = pd.DataFrame(klines, columns=[
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"open_time","open","high","low","close","volume",
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"close_time","quote_vol","trades","taker_buy_vol","taker_buy_quote_vol","ignore"
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])
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df_k["open_time"] = pd.to_datetime(df_k["open_time"], unit="ms")
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for c in ["open","high","low","close","volume","taker_buy_vol","taker_buy_quote_vol","quote_vol"]:
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df_k[c] = df_k[c].astype(float)
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df_k["kline_taker_buy_ratio"] = (df_k["taker_buy_vol"] / df_k["volume"]).replace([np.inf, -np.inf], np.nan)
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df_k["next_return"] = df_k["close"].shift(-1) / df_k["close"] - 1
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df_k["next_4_return"] = df_k["close"].shift(-4) / df_k["close"] - 1
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df_k = df_k.set_index("open_time")
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def join_ratio(data, col_name):
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if not data:
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return
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df = pd.DataFrame(data)
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df["timestamp"] = pd.to_datetime(df["timestamp"].astype(int), unit="ms")
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if "buySellRatio" in df.columns:
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df["buySellRatio"] = df["buySellRatio"].astype(float)
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df["buyVol"] = df["buyVol"].astype(float)
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df["sellVol"] = df["sellVol"].astype(float)
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df = df.set_index("timestamp")
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df_k.update(df_k.join(df[["buySellRatio","buyVol","sellVol"]], how="left"))
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for c in ["buySellRatio","buyVol","sellVol"]:
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if c not in df_k.columns:
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df_k[c] = np.nan
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joined = df_k.join(df[["buySellRatio","buyVol","sellVol"]], how="left", rsuffix="_new")
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for c in ["buySellRatio","buyVol","sellVol"]:
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if f"{c}_new" in joined.columns:
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df_k[c] = joined[f"{c}_new"]
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else:
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df["longShortRatio"] = df["longShortRatio"].astype(float)
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df = df.set_index("timestamp").rename(columns={"longShortRatio": col_name})
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df_k[col_name] = df_k.join(df[[col_name]], how="left")[col_name]
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join_ratio(taker, "buySellRatio")
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join_ratio(ls_ratio, "global_ls_ratio")
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join_ratio(top_acct, "top_acct_ls_ratio")
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join_ratio(top_pos, "top_pos_ls_ratio")
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return df_k
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def print_analysis(symbol, df_k):
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"""Print analysis results for a symbol"""
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print("\n" + "="*70)
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print(f"{symbol} {INTERVAL} Taker/Ratio → Price Correlation Analysis ({DAYS} days klines, ~5 days ratios)")
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print("="*70)
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features = ["kline_taker_buy_ratio", "buySellRatio", "global_ls_ratio",
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"top_acct_ls_ratio", "top_pos_ls_ratio"]
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available = [f for f in features if f in df_k.columns and df_k[f].notna().sum() > 20]
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# 1. Correlation
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print("\n[1] Pearson Correlation with Next-Candle Returns")
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print("-"*55)
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print(f"{'Feature':<25} {'next_15m':>12} {'next_1h':>12}")
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print("-"*55)
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for feat in available:
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c1 = df_k[feat].corr(df_k["next_return"])
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c4 = df_k[feat].corr(df_k["next_4_return"])
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print(f"{feat:<25} {c1:>12.4f} {c4:>12.4f}")
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# 2. Quintile - Taker
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print("\n[2] Taker Buy Ratio Quintile → Next Returns")
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print("-"*60)
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for ratio_col in ["kline_taker_buy_ratio", "buySellRatio"]:
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if ratio_col not in available:
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continue
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valid = df_k[[ratio_col, "next_return", "next_4_return"]].dropna()
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try:
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valid["quintile"] = pd.qcut(valid[ratio_col], 5, labels=["Q1(sell)","Q2","Q3","Q4","Q5(buy)"])
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except ValueError:
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continue
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print(f"\n {ratio_col}:")
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print(f" {'Quintile':<12} {'mean_ratio':>12} {'next_15m_bps':>14} {'next_1h_bps':>13} {'count':>7} {'win_rate':>10}")
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for q in ["Q1(sell)","Q2","Q3","Q4","Q5(buy)"]:
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grp = valid[valid["quintile"] == q]
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if len(grp) == 0:
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continue
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mr = grp[ratio_col].mean()
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r1 = grp["next_return"].mean() * 10000
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r4 = grp["next_4_return"].mean() * 10000
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wr = (grp["next_return"] > 0).mean() * 100
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print(f" {q:<12} {mr:>12.4f} {r1:>14.2f} {r4:>13.2f} {len(grp):>7} {wr:>9.1f}%")
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# 3. Extreme analysis
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print("\n[3] Extreme Taker Buy Ratio Analysis (top/bottom 10%)")
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print("-"*60)
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for ratio_col in ["kline_taker_buy_ratio", "buySellRatio"]:
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if ratio_col not in available:
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continue
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valid = df_k[[ratio_col, "next_return", "next_4_return"]].dropna()
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p10 = valid[ratio_col].quantile(0.10)
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p90 = valid[ratio_col].quantile(0.90)
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bottom = valid[valid[ratio_col] <= p10]
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top = valid[valid[ratio_col] >= p90]
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mid = valid[(valid[ratio_col] > p10) & (valid[ratio_col] < p90)]
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print(f"\n {ratio_col}:")
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print(f" {'Group':<18} {'mean_ratio':>12} {'next_15m_bps':>14} {'next_1h_bps':>13} {'win_rate':>10} {'count':>7}")
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for name, grp in [("Bottom 10% (sell)", bottom), ("Middle 80%", mid), ("Top 10% (buy)", top)]:
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if len(grp) == 0:
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continue
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mr = grp[ratio_col].mean()
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r1 = grp["next_return"].mean() * 10000
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r4 = grp["next_4_return"].mean() * 10000
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wr = (grp["next_return"] > 0).mean() * 100
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print(f" {name:<18} {mr:>12.4f} {r1:>14.2f} {r4:>13.2f} {wr:>9.1f}% {len(grp):>7}")
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# 4. L/S ratio quintile
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print("\n[4] Long/Short Ratio Quintile → Next Returns")
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print("-"*60)
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for ratio_col in ["global_ls_ratio", "top_acct_ls_ratio", "top_pos_ls_ratio"]:
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if ratio_col not in available:
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continue
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valid = df_k[[ratio_col, "next_return", "next_4_return"]].dropna()
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if len(valid) < 20:
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continue
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try:
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valid["quintile"] = pd.qcut(valid[ratio_col], 5, labels=["Q1(short)","Q2","Q3","Q4","Q5(long)"], duplicates="drop")
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except ValueError:
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continue
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print(f"\n {ratio_col}:")
|
||||
print(f" {'Quintile':<12} {'mean_ratio':>12} {'next_15m_bps':>14} {'next_1h_bps':>13} {'win_rate':>10} {'count':>7}")
|
||||
for q in valid["quintile"].cat.categories:
|
||||
grp = valid[valid["quintile"] == q]
|
||||
if len(grp) == 0:
|
||||
continue
|
||||
mr = grp[ratio_col].mean()
|
||||
r1 = grp["next_return"].mean() * 10000
|
||||
r4 = grp["next_4_return"].mean() * 10000
|
||||
wr = (grp["next_return"] > 0).mean() * 100
|
||||
print(f" {q:<12} {mr:>12.4f} {r1:>14.2f} {r4:>13.2f} {wr:>9.1f}% {len(grp):>7}")
|
||||
|
||||
# 5. Contrarian vs Momentum
|
||||
print("\n[5] Contrarian vs Momentum Signal Test")
|
||||
print("-"*60)
|
||||
for ratio_col, label in [("kline_taker_buy_ratio", "Taker Buy Ratio"),
|
||||
("global_ls_ratio", "Global L/S Ratio"),
|
||||
("top_acct_ls_ratio", "Top Trader Acct Ratio"),
|
||||
("top_pos_ls_ratio", "Top Trader Pos Ratio")]:
|
||||
if ratio_col not in available:
|
||||
continue
|
||||
valid = df_k[[ratio_col, "next_return", "next_4_return"]].dropna()
|
||||
median = valid[ratio_col].median()
|
||||
high = valid[valid[ratio_col] > median]
|
||||
low = valid[valid[ratio_col] <= median]
|
||||
h_wr = (high["next_return"] > 0).mean() * 100
|
||||
l_wr = (low["next_return"] > 0).mean() * 100
|
||||
h_r = high["next_return"].mean() * 10000
|
||||
l_r = low["next_return"].mean() * 10000
|
||||
signal = "Momentum" if h_r > l_r else "Contrarian"
|
||||
print(f"\n {label}:")
|
||||
print(f" Above median → next 15m: {h_r:+.2f} bps (win {h_wr:.1f}%)")
|
||||
print(f" Below median → next 15m: {l_r:+.2f} bps (win {l_wr:.1f}%)")
|
||||
print(f" → Signal type: {signal}")
|
||||
|
||||
# 6. Stats
|
||||
print("\n[6] Feature Statistics Summary")
|
||||
print("-"*60)
|
||||
for feat in available:
|
||||
s = df_k[feat].dropna()
|
||||
print(f" {feat}: mean={s.mean():.4f}, std={s.std():.4f}, min={s.min():.4f}, max={s.max():.4f}, n={len(s)}")
|
||||
print(f"\n Total klines: {len(df_k)}")
|
||||
print(f" Period: {df_k.index[0]} ~ {df_k.index[-1]}")
|
||||
|
||||
async def main():
|
||||
end_dt = datetime.now(timezone.utc)
|
||||
start_dt = end_dt - timedelta(days=DAYS)
|
||||
start_ms = int(start_dt.timestamp() * 1000)
|
||||
end_ms = int(end_dt.timestamp() * 1000)
|
||||
|
||||
print(f"Fetching {DAYS} days of {INTERVAL} data for {', '.join(SYMBOLS)}...")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
results = await asyncio.gather(
|
||||
*[analyze_symbol(session, sym, start_ms, end_ms) for sym in SYMBOLS]
|
||||
)
|
||||
|
||||
for sym, df in zip(SYMBOLS, results):
|
||||
print_analysis(sym, df)
|
||||
|
||||
# Cross-symbol comparison
|
||||
if len(SYMBOLS) > 1:
|
||||
print("\n" + "="*70)
|
||||
print("CROSS-SYMBOL COMPARISON SUMMARY")
|
||||
print("="*70)
|
||||
print(f"\n{'Symbol':<12} {'taker_buy→15m':>14} {'taker_buy→1h':>13} {'global_ls→1h':>13} {'top_acct→1h':>13} {'top_pos→1h':>12}")
|
||||
print("-"*78)
|
||||
for sym, df in zip(SYMBOLS, results):
|
||||
tb = df["kline_taker_buy_ratio"].corr(df["next_return"]) if "kline_taker_buy_ratio" in df.columns else float('nan')
|
||||
tb4 = df["kline_taker_buy_ratio"].corr(df["next_4_return"]) if "kline_taker_buy_ratio" in df.columns else float('nan')
|
||||
gl = df["global_ls_ratio"].corr(df["next_4_return"]) if "global_ls_ratio" in df.columns and df["global_ls_ratio"].notna().sum() > 20 else float('nan')
|
||||
ta = df["top_acct_ls_ratio"].corr(df["next_4_return"]) if "top_acct_ls_ratio" in df.columns and df["top_acct_ls_ratio"].notna().sum() > 20 else float('nan')
|
||||
tp = df["top_pos_ls_ratio"].corr(df["next_4_return"]) if "top_pos_ls_ratio" in df.columns and df["top_pos_ls_ratio"].notna().sum() > 20 else float('nan')
|
||||
print(f"{sym:<12} {tb:>14.4f} {tb4:>13.4f} {gl:>13.4f} {ta:>13.4f} {tp:>12.4f}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user