b9ee241d14
- 1분봉 다운로드 제외, MONITOR_PERSIST로 05/06 수집 시 coins.db INSERT - Phase C paper_fires 로그·07 모의 리포트, hybrid 시뮬 산출물·reference 문서 갱신 - .env Phase C(LIVE=0), bootstrap dotenv override=True Co-authored-by: Cursor <cursoragent@cursor.com>
555 lines
22 KiB
Python
555 lines
22 KiB
Python
import pandas as pd
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from deepcoin.api.bithumb import HTS
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from dateutil.relativedelta import relativedelta
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from datetime import datetime
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import sqlite3
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import time
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try:
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import telegram
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except ImportError:
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telegram = None # type: ignore
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import requests
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import json
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import asyncio
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from multiprocessing import Pool
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import numpy as np
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import os
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from config import *
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class Monitor(HTS):
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"""WLD 코인 데이터·지표·시장 상태 출력."""
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last_signal = None
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cooldown_file = None
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def __init__(self, cooldown_file: str | None = None) -> None:
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HTS.__init__(self)
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# 최근 매수 신호 저장용(파일은 [신규] 포맷으로 저장)
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self.last_signal: dict[str, str] = {}
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if cooldown_file is not None:
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self.cooldown_file = cooldown_file
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self.buy_cooldown = self._load_buy_cooldown()
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else:
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self.cooldown_file = None
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self.buy_cooldown = {}
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# ------------- Persistence -------------
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def _load_buy_cooldown(self) -> dict:
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"""load trade record file into nested dict {symbol:{'buy':{'datetime':dt,'signal':s},'sell':{...}}}"""
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if not os.path.exists(self.cooldown_file):
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return {}
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try:
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with open(self.cooldown_file, 'r', encoding='utf-8') as f:
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raw = json.load(f)
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except Exception as e:
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print(f"Error loading cooldown data: {e}")
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return {}
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record: dict[str, dict] = {}
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for symbol, value in raw.items():
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# 신규 포맷: value has 'buy'/'sell'
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if isinstance(value, dict) and ('buy' in value or 'sell' in value):
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record[symbol] = {}
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for side in ['buy', 'sell']:
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side_val = value.get(side)
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if isinstance(side_val, dict):
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dt_iso = side_val.get('datetime')
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sig = side_val.get('signal', '')
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if dt_iso:
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try:
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dt_obj = datetime.fromisoformat(dt_iso)
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except Exception:
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dt_obj = None
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else:
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dt_obj = None
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record[symbol][side] = {'datetime': dt_obj, 'signal': sig}
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else:
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# 구 포맷 처리 (매수만 기록)
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try:
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dt_obj = None
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sig = ''
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if isinstance(value, str):
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dt_obj = datetime.fromisoformat(value)
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elif isinstance(value, dict):
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dt_iso = value.get('datetime')
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sig = value.get('signal', '')
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if dt_iso:
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dt_obj = datetime.fromisoformat(dt_iso)
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record.setdefault(symbol, {})['buy'] = {'datetime': dt_obj, 'signal': sig}
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except Exception:
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continue
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# last_signal 채우기 (buy 기준)
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for sym, sides in record.items():
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if 'buy' in sides and sides['buy'].get('signal'):
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self.last_signal[sym] = sides['buy']['signal']
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return record
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def _save_buy_cooldown(self) -> None:
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"""save nested trade record structure"""
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try:
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data: dict[str, dict] = {}
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for symbol, sides in self.buy_cooldown.items():
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data[symbol] = {}
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for side in ['buy', 'sell']:
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info = sides.get(side)
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if not info:
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continue
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dt_obj = info.get('datetime')
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sig = info.get('signal', '')
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data[symbol][side] = {
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'datetime': dt_obj.isoformat() if isinstance(dt_obj, datetime) else '',
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'signal': sig,
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}
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with open(self.cooldown_file, 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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except Exception as e:
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print(f"Error saving cooldown data: {e}")
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# ------------- Telegram -------------
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def _send_coin_msg(self, text: str) -> None:
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if telegram is None:
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print(f"[telegram skip] {text}")
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return
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coin_client = telegram.Bot(token=COIN_TELEGRAM_BOT_TOKEN)
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asyncio.run(coin_client.send_message(chat_id=COIN_TELEGRAM_CHAT_ID, text=text))
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def sendMsg(self, msg):
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try:
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pool = Pool(12)
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pool.map(self._send_coin_msg, [msg])
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except Exception as e:
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print(f"Error sending Telegram message: {str(e)}")
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return
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def send_coin_telegram_message(self, message_list: list[str], header: str) -> None:
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payload = header + "\n"
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for i, message in enumerate(message_list):
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payload += message
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if i + 1 % MONITOR_TELEGRAM_BATCH_SIZE == 0:
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pool = Pool(MONITOR_POOL_WORKERS)
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pool.map(self._send_coin_msg, [payload])
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payload = ''
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if len(message_list) % MONITOR_TELEGRAM_BATCH_SIZE != 0:
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pool = Pool(MONITOR_POOL_WORKERS)
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pool.map(self._send_coin_msg, [payload])
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# ------------- Indicators -------------
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def normalize_data(self, data: pd.DataFrame) -> pd.DataFrame:
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columns_to_normalize = ['Open', 'High', 'Low', 'Close', 'Volume']
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normalized_data = data.copy()
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for column in columns_to_normalize:
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min_val = data[column].rolling(window=MONITOR_NORM_WINDOW).min()
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max_val = data[column].rolling(window=MONITOR_NORM_WINDOW).max()
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denominator = max_val - min_val
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normalized_data[f'{column}_Norm'] = np.where(
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denominator != 0,
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(data[column] - min_val) / denominator,
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0.5,
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)
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return normalized_data
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def inverse_data(self, data: pd.DataFrame) -> pd.DataFrame:
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"""원본 data 가격 시계를 상하 대칭(글로벌 min/max 기준)으로 반전하여 하락↔상승 트렌드를 뒤집는다."""
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price_cols = ['Open', 'High', 'Low', 'Close']
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inv = data.copy()
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global_min = data[price_cols].min().min()
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global_max = data[price_cols].max().max()
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# 축 기준은 global_mid = (max+min), so transformed = max+min - price
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for col in price_cols:
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inv[col] = global_max + global_min - data[col]
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# Volume은 그대로 유지
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inv['Volume'] = data['Volume']
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# 지표 다시 계산
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inv = self.normalize_data(inv)
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for w in MONITOR_MA_WINDOWS:
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inv[f"MA{w}"] = inv["Close"].rolling(window=w).mean()
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inv[f"Deviation{w}"] = (inv["Close"] / inv[f"MA{w}"]) * 100
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if len(MONITOR_MA_WINDOWS) >= 2:
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w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
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inv["golden_cross"] = (inv[f"MA{w_fast}"] > inv[f"MA{w_slow}"]) & (
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inv[f"MA{w_fast}"].shift(1) <= inv[f"MA{w_slow}"].shift(1)
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)
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inv["MA"] = inv["Close"].rolling(window=BB_PERIOD).mean()
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inv["STD"] = inv["Close"].rolling(window=BB_PERIOD).std()
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inv["Upper"] = inv["MA"] + (BB_STD * inv["STD"])
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inv["Lower"] = inv["MA"] - (BB_STD * inv["STD"])
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return inv
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def calculate_technical_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
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data = self.normalize_data(data)
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for w in MONITOR_MA_WINDOWS:
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data[f"MA{w}"] = data["Close"].rolling(window=w).mean()
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data[f"Deviation{w}"] = (data["Close"] / data[f"MA{w}"]) * 100
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if len(MONITOR_MA_WINDOWS) >= 2:
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w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
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data["golden_cross"] = (data[f"MA{w_fast}"] > data[f"MA{w_slow}"]) & (
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data[f"MA{w_fast}"].shift(1) <= data[f"MA{w_slow}"].shift(1)
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)
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data["MA"] = data["Close"].rolling(window=BB_PERIOD).mean()
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data["STD"] = data["Close"].rolling(window=BB_PERIOD).std()
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data["Upper"] = data["MA"] + (BB_STD * data["STD"])
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data["Lower"] = data["MA"] - (BB_STD * data["STD"])
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from deepcoin.common.indicators import add_macd, add_stochastic
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data = add_macd(data)
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data = add_stochastic(data)
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return data
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def process_wld_market_status(self, symbol: str) -> None:
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"""
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WLD: 전 봉 BB·일목 위치·추세만 출력 (자동 매매 없음).
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"""
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from deepcoin.common.candle_features import describe_latest_position
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from deepcoin.common.indicators import get_trend
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from deepcoin.data.mtf_bb import load_frames_from_db
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try:
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frames = load_frames_from_db(self, symbol)
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if not frames:
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print(f"Data for {symbol}: 로드된 봉 없음.")
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return
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df_1d = frames.get(TREND_INTERVAL_1D)
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df_1h = frames.get(TREND_INTERVAL_1H)
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if df_1d is None or df_1d.empty:
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df_1d = frames.get(ENTRY_INTERVAL)
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if df_1h is None or df_1h.empty:
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df_1h = frames.get(ENTRY_INTERVAL)
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trend = get_trend(df_1d, df_1h)
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print(f"{symbol} 추세(참고): {trend}")
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print("--- 봉별 BB·일목 위치 ---")
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for iv in sorted(frames.keys()):
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pos = describe_latest_position(frames[iv], iv)
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macd_s = ""
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if pos.get("macd_hist") is not None:
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macd_s = f" | MACD {pos.get('macd_state', '-')} h={pos['macd_hist']}"
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stoch_s = ""
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if pos.get("stoch_k") is not None:
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stoch_s = (
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f" | Stoch K={pos['stoch_k']} D={pos.get('stoch_d')} "
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f"{pos.get('stoch_zone', '')}"
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)
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disp_s = ""
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if pos.get("disparity"):
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parts = [f"{p}={v:.1f}" for p, v in sorted(pos["disparity"].items())]
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disp_s = " | D.I. " + " ".join(parts)
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print(
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f" {pos['label']:>6} | BB {pos['bb_zone']} {pos['bb_state']:>16} | "
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f"일목 {pos['ichi_position']} TK={pos['ichi_tk']}"
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f"{macd_s}{stoch_s}{disp_s}"
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)
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except Exception as e:
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print(f"Error processing {symbol}: {str(e)}")
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def process_symbol(
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self,
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symbol: str,
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interval: int | None = None,
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balances: dict | None = None,
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use_inverse: bool = False,
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) -> None:
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"""하위 호환: 시장 상태 출력으로 위임."""
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self.process_wld_market_status(symbol)
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def load_balances_dict(self) -> dict:
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"""getBalances() 결과를 currency 키 dict로 변환."""
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tmps = self.getBalances()
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balances = {}
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for tmp in tmps:
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balances[tmp["currency"]] = {
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"balance": float(tmp["balance"]),
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"avg_buy_price": float(tmp["avg_buy_price"]),
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}
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return balances
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# ------------- Formatting -------------
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def format_message(
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self, symbol: str, symbol_name: str, close: float, signal: str, buy_amount: float
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) -> str:
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message = f"[매수] {symbol_name} ({symbol}) [{signal}]: "
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if int(close) >= 100:
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message += f"₩{close}"
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message += f" (₩{buy_amount})"
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elif int(close) >= 10:
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message += f"₩{close:.2f}"
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message += f" (₩{buy_amount:.2f})"
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elif int(close) >= 1:
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message += f"₩{close:.3f}"
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message += f" (₩{buy_amount:.3f})"
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else:
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message += f"₩{close:.4f}"
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message += f" (₩{buy_amount:.4f})"
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if signal != '':
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message += f"[{signal}]"
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return message
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# ------------- Data fetch -------------
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def get_coin_data(
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self,
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symbol: str,
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interval: int = MONITOR_DEFAULT_INTERVAL,
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to: str | None = None,
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retries: int = MONITOR_API_RETRIES,
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) -> pd.DataFrame | None:
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base = BITHUMB_API_URL.rstrip("/")
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count = BITHUMB_API_CANDLE_COUNT
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for attempt in range(retries):
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try:
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if to is None:
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if interval >= DAILY_INTERVAL_MIN:
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url = f"{base}/v1/candles/days?market=KRW-{symbol}&count={count}"
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else:
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url = (
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f"{base}/v1/candles/minutes/{interval}"
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f"?market=KRW-{symbol}&count={count}"
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)
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else:
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if interval >= DAILY_INTERVAL_MIN:
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url = (
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f"{base}/v1/candles/days?market=KRW-{symbol}"
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f"&count={count}&to={to}"
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)
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else:
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url = (
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f"{base}/v1/candles/minutes/{interval}"
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f"?market=KRW-{symbol}&count={count}&to={to}"
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)
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headers = {"accept": "application/json"}
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response = requests.get(url, headers=headers)
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json_data = json.loads(response.text)
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df_temp = pd.DataFrame(json_data)
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df_temp = df_temp.sort_index(ascending=False)
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if 'candle_date_time_kst' not in df_temp:
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return None
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data = pd.DataFrame()
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data['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'], format='%Y-%m-%dT%H:%M:%S')
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data['Open'] = df_temp['opening_price']
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data['Close'] = df_temp['trade_price']
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data['High'] = df_temp['high_price']
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data['Low'] = df_temp['low_price']
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data['Volume'] = df_temp['candle_acc_trade_volume']
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data = data.set_index('datetime')
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data = data.astype(float)
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data["datetime"] = data.index
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if not data.empty:
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return data
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print(f"No data received for {symbol}, attempt {attempt + 1}")
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time.sleep(MONITOR_SLEEP_AFTER_REQUEST_SEC)
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except Exception as e:
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print(f"Attempt {attempt + 1} failed for {symbol}: {str(e)}")
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if attempt < retries - 1:
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time.sleep(MONITOR_SLEEP_RATE_LIMIT_SEC)
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continue
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return None
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def get_coin_more_data(
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self,
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symbol: str,
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interval: int,
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bong_count: int = MONITOR_API_BONG_COUNT,
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verbose: bool = False,
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) -> pd.DataFrame:
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"""
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빗썸 API를 반복 호출해 bong_count개까지 과거 봉을 수집합니다.
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Args:
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verbose: True면 수집 진행 상황을 출력합니다.
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"""
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to = datetime.now()
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data: pd.DataFrame | None = None
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step = 0
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while data is None or len(data) < bong_count:
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step += 1
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if data is None:
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chunk = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
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data = chunk
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else:
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previous_count = len(data)
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df = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
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if df is not None and not df.empty:
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data = pd.concat([data, df], ignore_index=True)
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if df is None or df.empty or previous_count == len(data):
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if verbose:
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print(f" API 추가 데이터 없음 (수집 {len(data)}봉)")
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break
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if verbose and (step == 1 or step % 5 == 0 or len(data) >= bong_count):
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label = "일봉" if interval >= 1440 else f"{interval}분"
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print(f" [{label}] 요청 {step}회 — 누적 {len(data)}/{bong_count}봉")
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time.sleep(MONITOR_SLEEP_BETWEEN_CHUNKS_SEC)
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to = to - relativedelta(minutes=interval * MONITOR_API_CHUNK_BARS)
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if data is None or data.empty:
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return pd.DataFrame()
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data = data.set_index("datetime")
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data = data.sort_index()
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data = data.drop_duplicates(keep="first")
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data["datetime"] = data.index
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return data
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@staticmethod
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def db_row_limit_for_interval(interval: int, lookback_days: int) -> int:
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"""
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lookback_days 구간 + 지표 워밍업을 담을 SQLite LIMIT(봉 개수)을 계산합니다.
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Args:
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interval: 봉 간격(분). 1440이면 일봉.
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lookback_days: 과거 조회 일수.
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Returns:
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LIMIT에 넣을 최대 행 수.
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"""
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if interval >= DAILY_INTERVAL_MIN:
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return max(
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lookback_days + DB_ROW_DAILY_PADDING_DAYS,
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DB_ROW_MIN_DAILY_BARS,
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)
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bars_per_day = max((24 * 60) // max(interval, 1), 1)
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return bars_per_day * lookback_days + DB_ROW_WARMUP_BARS
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def persist_api_candles_to_db(
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self,
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symbol: str,
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interval: int,
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data: pd.DataFrame,
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db_path: str = DB_PATH,
|
|
) -> tuple[int, int]:
|
|
"""
|
|
API로 받은 봉을 coins.db에 증분 INSERT합니다 (01_download.append_data와 동일).
|
|
|
|
dry-run·05·06·live_eval이 load_frames_from_db 할 때마다 최신 봉이 쌓입니다.
|
|
|
|
Returns:
|
|
(추가 행 수, 스킵 행 수)
|
|
"""
|
|
if not MONITOR_PERSIST_CANDLES or data is None or data.empty:
|
|
return 0, 0
|
|
|
|
from deepcoin.data.downloader import (
|
|
append_data,
|
|
get_last_timestamp,
|
|
months_for_interval,
|
|
prune_before_cutoff,
|
|
)
|
|
|
|
if not isinstance(data.index, pd.DatetimeIndex):
|
|
data = data.copy()
|
|
data.index = pd.to_datetime(data.index)
|
|
data = data.sort_index()
|
|
|
|
last_ts = get_last_timestamp(symbol, interval, db_path=db_path)
|
|
inserted, skipped = append_data(
|
|
symbol, interval, data, last_ts=last_ts, db_path=db_path
|
|
)
|
|
if inserted > 0:
|
|
months = months_for_interval(interval, DOWNLOAD_MONTHS)
|
|
prune_before_cutoff(symbol, interval, months, db_path=db_path)
|
|
return inserted, skipped
|
|
|
|
def read_candles_from_db(
|
|
self,
|
|
symbol: str,
|
|
interval: int,
|
|
db_path: str = DB_PATH,
|
|
max_rows: int = DB_READ_LIMIT_DEFAULT,
|
|
) -> pd.DataFrame:
|
|
"""
|
|
coins.db에서 저장된 봉을 읽습니다.
|
|
|
|
scripts/01_download.py 또는 persist_api_candles_to_db로 적재된 데이터.
|
|
"""
|
|
conn = sqlite3.connect(db_path)
|
|
cursor = conn.cursor()
|
|
table_name = f"{symbol}_{interval}"
|
|
cursor.execute(
|
|
f"CREATE TABLE IF NOT EXISTS {table_name} "
|
|
"(CODE text, NAME text, ymdhms datetime, ymd text, hms text, "
|
|
"Close REAL, Open REAL, High REAL, Low REAL, Volume REAL)"
|
|
)
|
|
cursor.execute(
|
|
f"CREATE INDEX IF NOT EXISTS {table_name}_idx ON {table_name}(CODE, ymdhms)"
|
|
)
|
|
|
|
cursor.execute(
|
|
f"SELECT Open, Close, High, Low, Volume, ymdhms AS datetime "
|
|
f"FROM (SELECT Open, Close, High, Low, Volume, ymdhms "
|
|
f"FROM {table_name} ORDER BY ymdhms DESC LIMIT {int(max_rows)}) "
|
|
f"ORDER BY datetime"
|
|
)
|
|
result = cursor.fetchall()
|
|
conn.commit()
|
|
cursor.close()
|
|
conn.close()
|
|
|
|
if not result:
|
|
return pd.DataFrame(
|
|
columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
|
|
)
|
|
|
|
df = pd.DataFrame(
|
|
result, columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
|
|
)
|
|
df = df.set_index("datetime")
|
|
df = df.sort_index()
|
|
df["datetime"] = df.index
|
|
return df
|
|
|
|
def get_coin_saved_data(
|
|
self,
|
|
symbol: str,
|
|
interval: int,
|
|
data: pd.DataFrame,
|
|
db_path: str = DB_PATH,
|
|
max_rows: int = DB_READ_LIMIT_DEFAULT,
|
|
) -> pd.DataFrame:
|
|
"""하위 호환: API 봉 저장 후 DB에서 읽기."""
|
|
self.persist_api_candles_to_db(symbol, interval, data, db_path=db_path)
|
|
return self.read_candles_from_db(
|
|
symbol, interval, db_path=db_path, max_rows=max_rows
|
|
)
|
|
|
|
def get_coin_some_data(
|
|
self, symbol: str, interval: int, db_max_rows: int | None = None
|
|
) -> pd.DataFrame:
|
|
"""
|
|
WLD 시세: API 최신 봉 + coins.db 과거 봉 + 1분봉 최신 1개를 합칩니다.
|
|
|
|
MONITOR_PERSIST_CANDLES=1 이면 API 청크를 즉시 coins.db에 INSERT합니다.
|
|
"""
|
|
data = self.get_coin_data(symbol, interval)
|
|
if data is None or data.empty:
|
|
return pd.DataFrame()
|
|
|
|
self.persist_api_candles_to_db(symbol, interval, data)
|
|
|
|
data_1 = self.get_coin_data(symbol, interval=1)
|
|
if data_1 is not None and not data_1.empty:
|
|
data_1 = data_1.copy()
|
|
data_1.at[data_1.index[-1], "Volume"] = data_1["Volume"].iloc[-1] * 60
|
|
|
|
row_limit = DB_READ_LIMIT_DEFAULT if db_max_rows is None else int(db_max_rows)
|
|
saved_data = self.read_candles_from_db(
|
|
symbol, interval, max_rows=row_limit
|
|
)
|
|
parts = [data]
|
|
if saved_data is not None and not saved_data.empty:
|
|
parts.append(saved_data)
|
|
if data_1 is not None and not data_1.empty:
|
|
parts.append(data_1.iloc[[-1]])
|
|
|
|
merged = pd.concat(parts, ignore_index=True)
|
|
merged["datetime"] = pd.to_datetime(merged["datetime"], format="%Y-%m-%d %H:%M:%S")
|
|
merged = merged.set_index("datetime")
|
|
merged = merged.sort_index()
|
|
merged = merged.drop_duplicates(keep="first")
|
|
merged["datetime"] = merged.index
|
|
return merged
|