555 lines
22 KiB
Python
555 lines
22 KiB
Python
import pandas as pd
|
|
from deepcoin.api.bithumb import HTS
|
|
from dateutil.relativedelta import relativedelta
|
|
from datetime import datetime
|
|
import sqlite3
|
|
import time
|
|
|
|
try:
|
|
import telegram
|
|
except ImportError:
|
|
telegram = None # type: ignore
|
|
import requests
|
|
import json
|
|
import asyncio
|
|
from multiprocessing import Pool
|
|
import numpy as np
|
|
import os
|
|
|
|
from config import *
|
|
from deepcoin.data.candle_intervals import (
|
|
candle_api_segment,
|
|
interval_display_label,
|
|
pagination_step,
|
|
)
|
|
|
|
class Monitor(HTS):
|
|
"""WLD 코인 데이터·지표·시장 상태 출력."""
|
|
|
|
last_signal = None
|
|
cooldown_file = None
|
|
|
|
def __init__(self, cooldown_file: str | None = None) -> None:
|
|
HTS.__init__(self)
|
|
# 최근 매수 신호 저장용(파일은 [신규] 포맷으로 저장)
|
|
self.last_signal: dict[str, str] = {}
|
|
if cooldown_file is not None:
|
|
self.cooldown_file = cooldown_file
|
|
self.buy_cooldown = self._load_buy_cooldown()
|
|
else:
|
|
self.cooldown_file = None
|
|
self.buy_cooldown = {}
|
|
|
|
# ------------- Persistence -------------
|
|
def _load_buy_cooldown(self) -> dict:
|
|
"""load trade record file into nested dict {symbol:{'buy':{'datetime':dt,'signal':s},'sell':{...}}}"""
|
|
if not os.path.exists(self.cooldown_file):
|
|
return {}
|
|
|
|
try:
|
|
with open(self.cooldown_file, 'r', encoding='utf-8') as f:
|
|
raw = json.load(f)
|
|
except Exception as e:
|
|
print(f"Error loading cooldown data: {e}")
|
|
return {}
|
|
|
|
record: dict[str, dict] = {}
|
|
for symbol, value in raw.items():
|
|
# 신규 포맷: value has 'buy'/'sell'
|
|
if isinstance(value, dict) and ('buy' in value or 'sell' in value):
|
|
record[symbol] = {}
|
|
for side in ['buy', 'sell']:
|
|
side_val = value.get(side)
|
|
if isinstance(side_val, dict):
|
|
dt_iso = side_val.get('datetime')
|
|
sig = side_val.get('signal', '')
|
|
if dt_iso:
|
|
try:
|
|
dt_obj = datetime.fromisoformat(dt_iso)
|
|
except Exception:
|
|
dt_obj = None
|
|
else:
|
|
dt_obj = None
|
|
record[symbol][side] = {'datetime': dt_obj, 'signal': sig}
|
|
else:
|
|
# 구 포맷 처리 (매수만 기록)
|
|
try:
|
|
dt_obj = None
|
|
sig = ''
|
|
if isinstance(value, str):
|
|
dt_obj = datetime.fromisoformat(value)
|
|
elif isinstance(value, dict):
|
|
dt_iso = value.get('datetime')
|
|
sig = value.get('signal', '')
|
|
if dt_iso:
|
|
dt_obj = datetime.fromisoformat(dt_iso)
|
|
record.setdefault(symbol, {})['buy'] = {'datetime': dt_obj, 'signal': sig}
|
|
except Exception:
|
|
continue
|
|
|
|
# last_signal 채우기 (buy 기준)
|
|
for sym, sides in record.items():
|
|
if 'buy' in sides and sides['buy'].get('signal'):
|
|
self.last_signal[sym] = sides['buy']['signal']
|
|
return record
|
|
|
|
def _save_buy_cooldown(self) -> None:
|
|
"""save nested trade record structure"""
|
|
try:
|
|
data: dict[str, dict] = {}
|
|
for symbol, sides in self.buy_cooldown.items():
|
|
data[symbol] = {}
|
|
for side in ['buy', 'sell']:
|
|
info = sides.get(side)
|
|
if not info:
|
|
continue
|
|
dt_obj = info.get('datetime')
|
|
sig = info.get('signal', '')
|
|
data[symbol][side] = {
|
|
'datetime': dt_obj.isoformat() if isinstance(dt_obj, datetime) else '',
|
|
'signal': sig,
|
|
}
|
|
with open(self.cooldown_file, 'w', encoding='utf-8') as f:
|
|
json.dump(data, f, ensure_ascii=False, indent=2)
|
|
except Exception as e:
|
|
print(f"Error saving cooldown data: {e}")
|
|
|
|
# ------------- Telegram -------------
|
|
def _send_coin_msg(self, text: str) -> None:
|
|
if telegram is None:
|
|
print(f"[telegram skip] {text}")
|
|
return
|
|
coin_client = telegram.Bot(token=COIN_TELEGRAM_BOT_TOKEN)
|
|
asyncio.run(coin_client.send_message(chat_id=COIN_TELEGRAM_CHAT_ID, text=text))
|
|
|
|
def sendMsg(self, msg):
|
|
try:
|
|
pool = Pool(12)
|
|
pool.map(self._send_coin_msg, [msg])
|
|
except Exception as e:
|
|
print(f"Error sending Telegram message: {str(e)}")
|
|
return
|
|
|
|
def send_coin_telegram_message(self, message_list: list[str], header: str) -> None:
|
|
payload = header + "\n"
|
|
for i, message in enumerate(message_list):
|
|
payload += message
|
|
if i + 1 % MONITOR_TELEGRAM_BATCH_SIZE == 0:
|
|
pool = Pool(MONITOR_POOL_WORKERS)
|
|
pool.map(self._send_coin_msg, [payload])
|
|
payload = ''
|
|
if len(message_list) % MONITOR_TELEGRAM_BATCH_SIZE != 0:
|
|
pool = Pool(MONITOR_POOL_WORKERS)
|
|
pool.map(self._send_coin_msg, [payload])
|
|
|
|
# ------------- Indicators -------------
|
|
def normalize_data(self, data: pd.DataFrame) -> pd.DataFrame:
|
|
columns_to_normalize = ['Open', 'High', 'Low', 'Close', 'Volume']
|
|
normalized_data = data.copy()
|
|
for column in columns_to_normalize:
|
|
min_val = data[column].rolling(window=MONITOR_NORM_WINDOW).min()
|
|
max_val = data[column].rolling(window=MONITOR_NORM_WINDOW).max()
|
|
denominator = max_val - min_val
|
|
normalized_data[f'{column}_Norm'] = np.where(
|
|
denominator != 0,
|
|
(data[column] - min_val) / denominator,
|
|
0.5,
|
|
)
|
|
return normalized_data
|
|
|
|
def inverse_data(self, data: pd.DataFrame) -> pd.DataFrame:
|
|
"""원본 data 가격 시계를 상하 대칭(글로벌 min/max 기준)으로 반전하여 하락↔상승 트렌드를 뒤집는다."""
|
|
price_cols = ['Open', 'High', 'Low', 'Close']
|
|
inv = data.copy()
|
|
global_min = data[price_cols].min().min()
|
|
global_max = data[price_cols].max().max()
|
|
# 축 기준은 global_mid = (max+min), so transformed = max+min - price
|
|
for col in price_cols:
|
|
inv[col] = global_max + global_min - data[col]
|
|
# Volume은 그대로 유지
|
|
inv['Volume'] = data['Volume']
|
|
# 지표 다시 계산
|
|
inv = self.normalize_data(inv)
|
|
|
|
for w in MONITOR_MA_WINDOWS:
|
|
inv[f"MA{w}"] = inv["Close"].rolling(window=w).mean()
|
|
inv[f"Deviation{w}"] = (inv["Close"] / inv[f"MA{w}"]) * 100
|
|
if len(MONITOR_MA_WINDOWS) >= 2:
|
|
w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
|
|
inv["golden_cross"] = (inv[f"MA{w_fast}"] > inv[f"MA{w_slow}"]) & (
|
|
inv[f"MA{w_fast}"].shift(1) <= inv[f"MA{w_slow}"].shift(1)
|
|
)
|
|
inv["MA"] = inv["Close"].rolling(window=BB_PERIOD).mean()
|
|
inv["STD"] = inv["Close"].rolling(window=BB_PERIOD).std()
|
|
inv["Upper"] = inv["MA"] + (BB_STD * inv["STD"])
|
|
inv["Lower"] = inv["MA"] - (BB_STD * inv["STD"])
|
|
return inv
|
|
|
|
def calculate_technical_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
|
|
data = self.normalize_data(data)
|
|
|
|
for w in MONITOR_MA_WINDOWS:
|
|
data[f"MA{w}"] = data["Close"].rolling(window=w).mean()
|
|
data[f"Deviation{w}"] = (data["Close"] / data[f"MA{w}"]) * 100
|
|
if len(MONITOR_MA_WINDOWS) >= 2:
|
|
w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
|
|
data["golden_cross"] = (data[f"MA{w_fast}"] > data[f"MA{w_slow}"]) & (
|
|
data[f"MA{w_fast}"].shift(1) <= data[f"MA{w_slow}"].shift(1)
|
|
)
|
|
data["MA"] = data["Close"].rolling(window=BB_PERIOD).mean()
|
|
data["STD"] = data["Close"].rolling(window=BB_PERIOD).std()
|
|
data["Upper"] = data["MA"] + (BB_STD * data["STD"])
|
|
data["Lower"] = data["MA"] - (BB_STD * data["STD"])
|
|
|
|
from deepcoin.common.indicators import add_macd, add_stochastic
|
|
|
|
data = add_macd(data)
|
|
data = add_stochastic(data)
|
|
return data
|
|
|
|
def process_wld_market_status(self, symbol: str) -> None:
|
|
"""
|
|
WLD: 전 봉 BB·일목 위치·추세만 출력 (자동 매매 없음).
|
|
"""
|
|
from deepcoin.common.candle_features import describe_latest_position
|
|
from deepcoin.common.indicators import get_trend
|
|
from deepcoin.data.mtf_bb import load_frames_from_db
|
|
|
|
try:
|
|
frames = load_frames_from_db(self, symbol)
|
|
if not frames:
|
|
print(f"Data for {symbol}: 로드된 봉 없음.")
|
|
return
|
|
|
|
df_1d = frames.get(TREND_INTERVAL_1D)
|
|
df_1h = frames.get(TREND_INTERVAL_1H)
|
|
if df_1d is None or df_1d.empty:
|
|
df_1d = frames.get(ENTRY_INTERVAL)
|
|
if df_1h is None or df_1h.empty:
|
|
df_1h = frames.get(ENTRY_INTERVAL)
|
|
|
|
trend = get_trend(df_1d, df_1h)
|
|
print(f"{symbol} 추세(참고): {trend}")
|
|
print("--- 봉별 BB·일목 위치 ---")
|
|
for iv in sorted(frames.keys()):
|
|
pos = describe_latest_position(frames[iv], iv)
|
|
macd_s = ""
|
|
if pos.get("macd_hist") is not None:
|
|
macd_s = f" | MACD {pos.get('macd_state', '-')} h={pos['macd_hist']}"
|
|
stoch_s = ""
|
|
if pos.get("stoch_k") is not None:
|
|
stoch_s = (
|
|
f" | Stoch K={pos['stoch_k']} D={pos.get('stoch_d')} "
|
|
f"{pos.get('stoch_zone', '')}"
|
|
)
|
|
disp_s = ""
|
|
if pos.get("disparity"):
|
|
parts = [f"{p}={v:.1f}" for p, v in sorted(pos["disparity"].items())]
|
|
disp_s = " | D.I. " + " ".join(parts)
|
|
print(
|
|
f" {pos['label']:>6} | BB {pos['bb_zone']} {pos['bb_state']:>16} | "
|
|
f"일목 {pos['ichi_position']} TK={pos['ichi_tk']}"
|
|
f"{macd_s}{stoch_s}{disp_s}"
|
|
)
|
|
except Exception as e:
|
|
print(f"Error processing {symbol}: {str(e)}")
|
|
|
|
def process_symbol(
|
|
self,
|
|
symbol: str,
|
|
interval: int | None = None,
|
|
balances: dict | None = None,
|
|
use_inverse: bool = False,
|
|
) -> None:
|
|
"""하위 호환: 시장 상태 출력으로 위임."""
|
|
self.process_wld_market_status(symbol)
|
|
|
|
def load_balances_dict(self) -> dict:
|
|
"""getBalances() 결과를 currency 키 dict로 변환."""
|
|
tmps = self.getBalances()
|
|
balances: dict = {}
|
|
if isinstance(tmps, dict):
|
|
if tmps.get("error"):
|
|
raise RuntimeError(f"getBalances: {tmps.get('error')}")
|
|
return balances
|
|
if not isinstance(tmps, list):
|
|
raise RuntimeError(f"getBalances unexpected: {type(tmps)}")
|
|
for tmp in tmps:
|
|
if not isinstance(tmp, dict) or "currency" not in tmp:
|
|
continue
|
|
balances[tmp["currency"]] = {
|
|
"balance": float(tmp["balance"]),
|
|
"avg_buy_price": float(tmp.get("avg_buy_price") or 0),
|
|
}
|
|
return balances
|
|
|
|
# ------------- Formatting -------------
|
|
def format_message(
|
|
self, symbol: str, symbol_name: str, close: float, signal: str, buy_amount: float
|
|
) -> str:
|
|
message = f"[매수] {symbol_name} ({symbol}) [{signal}]: "
|
|
|
|
if int(close) >= 100:
|
|
message += f"₩{close}"
|
|
message += f" (₩{buy_amount})"
|
|
elif int(close) >= 10:
|
|
message += f"₩{close:.2f}"
|
|
message += f" (₩{buy_amount:.2f})"
|
|
elif int(close) >= 1:
|
|
message += f"₩{close:.3f}"
|
|
message += f" (₩{buy_amount:.3f})"
|
|
else:
|
|
message += f"₩{close:.4f}"
|
|
message += f" (₩{buy_amount:.4f})"
|
|
|
|
if signal != '':
|
|
message += f"[{signal}]"
|
|
return message
|
|
|
|
# ------------- Data fetch -------------
|
|
def get_coin_data(
|
|
self,
|
|
symbol: str,
|
|
interval: int = MONITOR_DEFAULT_INTERVAL,
|
|
to: str | None = None,
|
|
retries: int = MONITOR_API_RETRIES,
|
|
) -> pd.DataFrame | None:
|
|
base = BITHUMB_API_URL.rstrip("/")
|
|
count = BITHUMB_API_CANDLE_COUNT
|
|
segment = candle_api_segment(interval)
|
|
for attempt in range(retries):
|
|
try:
|
|
path = f"/v1/candles/{segment}"
|
|
if to is None:
|
|
url = f"{base}{path}?market=KRW-{symbol}&count={count}"
|
|
else:
|
|
url = f"{base}{path}?market=KRW-{symbol}&count={count}&to={to}"
|
|
headers = {"accept": "application/json"}
|
|
response = requests.get(url, headers=headers)
|
|
json_data = json.loads(response.text)
|
|
df_temp = pd.DataFrame(json_data)
|
|
df_temp = df_temp.sort_index(ascending=False)
|
|
if 'candle_date_time_kst' not in df_temp:
|
|
return None
|
|
data = pd.DataFrame()
|
|
data['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'], format='%Y-%m-%dT%H:%M:%S')
|
|
data['Open'] = df_temp['opening_price']
|
|
data['Close'] = df_temp['trade_price']
|
|
data['High'] = df_temp['high_price']
|
|
data['Low'] = df_temp['low_price']
|
|
data['Volume'] = df_temp['candle_acc_trade_volume']
|
|
data = data.set_index('datetime')
|
|
data = data.astype(float)
|
|
data["datetime"] = data.index
|
|
if not data.empty:
|
|
return data
|
|
print(f"No data received for {symbol}, attempt {attempt + 1}")
|
|
time.sleep(MONITOR_SLEEP_AFTER_REQUEST_SEC)
|
|
except Exception as e:
|
|
print(f"Attempt {attempt + 1} failed for {symbol}: {str(e)}")
|
|
if attempt < retries - 1:
|
|
time.sleep(MONITOR_SLEEP_RATE_LIMIT_SEC)
|
|
continue
|
|
return None
|
|
|
|
def get_coin_more_data(
|
|
self,
|
|
symbol: str,
|
|
interval: int,
|
|
bong_count: int = MONITOR_API_BONG_COUNT,
|
|
verbose: bool = False,
|
|
) -> pd.DataFrame:
|
|
"""
|
|
빗썸 API를 반복 호출해 bong_count개까지 과거 봉을 수집합니다.
|
|
|
|
Args:
|
|
verbose: True면 수집 진행 상황을 출력합니다.
|
|
"""
|
|
to = datetime.now()
|
|
data: pd.DataFrame | None = None
|
|
step = 0
|
|
while data is None or len(data) < bong_count:
|
|
step += 1
|
|
if data is None:
|
|
chunk = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
|
|
data = chunk
|
|
else:
|
|
previous_count = len(data)
|
|
df = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
|
|
if df is not None and not df.empty:
|
|
data = pd.concat([data, df], ignore_index=True)
|
|
if df is None or df.empty or previous_count == len(data):
|
|
if verbose:
|
|
print(f" API 추가 데이터 없음 (수집 {len(data)}봉)")
|
|
break
|
|
if verbose and (step == 1 or step % 5 == 0 or len(data) >= bong_count):
|
|
label = interval_display_label(interval)
|
|
print(f" [{label}] 요청 {step}회 — 누적 {len(data)}/{bong_count}봉")
|
|
time.sleep(MONITOR_SLEEP_BETWEEN_CHUNKS_SEC)
|
|
to = to - pagination_step(interval, MONITOR_API_CHUNK_BARS)
|
|
if data is None or data.empty:
|
|
return pd.DataFrame()
|
|
data = data.set_index("datetime")
|
|
data = data.sort_index()
|
|
data = data.drop_duplicates(keep="first")
|
|
data["datetime"] = data.index
|
|
return data
|
|
|
|
@staticmethod
|
|
def db_row_limit_for_interval(interval: int, lookback_days: int) -> int:
|
|
"""
|
|
lookback_days 구간 + 지표 워밍업을 담을 SQLite LIMIT(봉 개수)을 계산합니다.
|
|
|
|
Args:
|
|
interval: 봉 간격(분). 1440이면 일봉.
|
|
lookback_days: 과거 조회 일수.
|
|
|
|
Returns:
|
|
LIMIT에 넣을 최대 행 수.
|
|
"""
|
|
from config import MONTH_INTERVAL_MIN, WEEK_INTERVAL_MIN
|
|
|
|
if interval == WEEK_INTERVAL_MIN:
|
|
return max(lookback_days // 7 + 10, DB_ROW_MIN_DAILY_BARS)
|
|
if interval == MONTH_INTERVAL_MIN:
|
|
return max(lookback_days // 30 + 6, DB_ROW_MIN_DAILY_BARS)
|
|
if interval >= DAILY_INTERVAL_MIN:
|
|
return max(
|
|
lookback_days + DB_ROW_DAILY_PADDING_DAYS,
|
|
DB_ROW_MIN_DAILY_BARS,
|
|
)
|
|
bars_per_day = max((24 * 60) // max(interval, 1), 1)
|
|
return bars_per_day * lookback_days + DB_ROW_WARMUP_BARS
|
|
|
|
def persist_api_candles_to_db(
|
|
self,
|
|
symbol: str,
|
|
interval: int,
|
|
data: pd.DataFrame,
|
|
db_path: str = DB_PATH,
|
|
) -> tuple[int, int]:
|
|
"""
|
|
API로 받은 봉을 coins.db에 증분 INSERT합니다 (01_download.append_data와 동일).
|
|
|
|
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 봉을 합칩니다.
|
|
|
|
MONITOR_PERSIST_CANDLES=1 이면 API 청크를 즉시 coins.db에 INSERT합니다.
|
|
1분봉은 다운로드·병합하지 않습니다.
|
|
"""
|
|
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)
|
|
|
|
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)
|
|
|
|
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
|