Spaces:
Running
Running
upto date!
Browse files- vcp_debug.py +184 -126
vcp_debug.py
CHANGED
|
@@ -1,24 +1,22 @@
|
|
| 1 |
# -*- coding: utf-8 -*-
|
| 2 |
"""
|
| 3 |
-
VCP
|
| 4 |
TICKER ํ๋๋ง ๋ฐ๊พธ๋ฉด ๋จ!
|
| 5 |
|
| 6 |
-
|
| 7 |
-
1.
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
3. ํญ๋ฐ์ผ ์์น๋ฅ (์ ์ผ ์ข
๊ฐ ๋๋น) >= MIN_PRICE_GAIN
|
| 12 |
-
4. ํญ๋ฐ์ผ ์๋ด (์ข
๊ฐ > ์๊ฐ)
|
| 13 |
-
|
| 14 |
-
โ
์ด ํ์ผ์ '๊ฐ์ง ์ค์ ' ๋ธ๋ก์ vcp_sell_debug.py ์ ๋๊ฐ์ด ์ ์งํ ๊ฒ!
|
| 15 |
"""
|
| 16 |
|
| 17 |
import sys, ssl
|
| 18 |
import pandas as pd
|
| 19 |
import numpy as np
|
| 20 |
import FinanceDataReader as fdr
|
| 21 |
-
from datetime import datetime, timedelta
|
|
|
|
|
|
|
| 22 |
|
| 23 |
ssl._create_default_https_context = ssl._create_unverified_context
|
| 24 |
if sys.platform == 'win32':
|
|
@@ -27,137 +25,197 @@ if sys.platform == 'win32':
|
|
| 27 |
# =============================================
|
| 28 |
# [์ฌ๊ธฐ๋ง ๋ฐ๊พธ๋ฉด ๋จ]
|
| 29 |
# =============================================
|
| 30 |
-
TICKER = '
|
| 31 |
|
| 32 |
# =============================================
|
| 33 |
-
#
|
| 34 |
# =============================================
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
MIN_PRICE_GAIN
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
|
|
|
| 43 |
|
| 44 |
# =============================================
|
| 45 |
-
#
|
| 46 |
# =============================================
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
ANALYZE_FROM = (datetime.today() - timedelta(days=ANALYZE_DAYS)).strftime('%Y-%m-%d')
|
| 51 |
-
ANALYZE_TO = datetime.today().strftime('%Y-%m-%d')
|
| 52 |
|
|
|
|
|
|
|
|
|
|
| 53 |
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
L = df['Low'].values.astype(float); C = df['Close'].values.astype(float)
|
| 60 |
-
V = df['Volume'].values.astype(float)
|
| 61 |
-
out = []
|
| 62 |
-
for t in range(BASE_LOOKBACK, len(df)):
|
| 63 |
-
quiet_max = V[t - CONTRACT_DAYS:t].max() # ์ง์ ์กฐ์ฉํ ๋ ๋ค ์ค '์ต๋' ๊ฑฐ๋๋
|
| 64 |
-
if quiet_max <= 0 or C[t - 1] <= 0:
|
| 65 |
-
continue
|
| 66 |
-
surge = V[t] / quiet_max # ์ง์ ๋ฉฐ์น '๊ฐ๊ฐ'์ N๋ฐฐ (ํ๋๋ผ๋ ํฌ๋ฉด ํ๋ฝ = ํญ๋ฐ ๋ค์๋ ๋ฐฉ์ง)
|
| 67 |
-
base_high = H[t - BASE_LOOKBACK:t].max()
|
| 68 |
-
gain = C[t] / C[t - 1] - 1
|
| 69 |
-
if (surge >= VOL_DRYUP_SURGE and C[t] > base_high
|
| 70 |
-
and gain >= MIN_PRICE_GAIN and C[t] > O[t]):
|
| 71 |
-
out.append({
|
| 72 |
-
'i': t, 'date': str(df.index[t])[:10],
|
| 73 |
-
'close': C[t], 'open': O[t], 'low': L[t],
|
| 74 |
-
'gain': round(gain * 100, 1), 'surge': round(surge, 2),
|
| 75 |
-
'base_high': base_high, 'base_low': float(L[t - BASE_LOOKBACK:t].min()),
|
| 76 |
-
})
|
| 77 |
-
return [b for b in out if b['date'] >= ANALYZE_FROM]
|
| 78 |
|
|
|
|
| 79 |
|
| 80 |
# =============================================
|
| 81 |
-
#
|
| 82 |
# =============================================
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
|
| 129 |
-
if
|
| 130 |
-
|
| 131 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 132 |
|
| 133 |
# =============================================
|
| 134 |
-
#
|
| 135 |
# =============================================
|
| 136 |
-
print("
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
print("
|
| 145 |
-
print(f"
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 149 |
|
| 150 |
-
|
| 151 |
-
print("
|
| 152 |
-
print(f"
|
| 153 |
-
|
| 154 |
-
|
|
|
|
|
|
|
| 155 |
print()
|
| 156 |
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
for
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
|
| 163 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# -*- coding: utf-8 -*-
|
| 2 |
"""
|
| 3 |
+
VCP (Volatility Contraction Pattern) - ๋ฏธ๋๋น๋ ๊ธฐ์ค
|
| 4 |
TICKER ํ๋๋ง ๋ฐ๊พธ๋ฉด ๋จ!
|
| 5 |
|
| 6 |
+
๋ํ ์กฐ๊ฑด (๋ชจ๋ ์ถฉ์กฑํด์ผ):
|
| 7 |
+
1. ์ข
๊ฐ๊ฐ ์์ถ๊ตฌ๊ฐ ๊ณ ๊ฐ๋ฅผ ๋ํ
|
| 8 |
+
2. ๋น์ผ ๊ฐ๊ฒฉ ์์น๋ฅ 3% ์ด์ (์ ์ผ ์ข
๊ฐ ๋๋น)
|
| 9 |
+
3. ๋น์ผ ์๋ด (์ข
๊ฐ > ์๊ฐ)
|
| 10 |
+
4. ๊ฑฐ๋๋ 20์ผMA ร 2.0 ์ด์ ํญ๋ฐ
|
|
|
|
|
|
|
|
|
|
|
|
|
| 11 |
"""
|
| 12 |
|
| 13 |
import sys, ssl
|
| 14 |
import pandas as pd
|
| 15 |
import numpy as np
|
| 16 |
import FinanceDataReader as fdr
|
| 17 |
+
from datetime import datetime, timedelta, timezone
|
| 18 |
+
_KST = timezone(timedelta(hours=9))
|
| 19 |
+
def _now_kst(): return datetime.now(_KST)
|
| 20 |
|
| 21 |
ssl._create_default_https_context = ssl._create_unverified_context
|
| 22 |
if sys.platform == 'win32':
|
|
|
|
| 25 |
# =============================================
|
| 26 |
# [์ฌ๊ธฐ๋ง ๋ฐ๊พธ๋ฉด ๋จ]
|
| 27 |
# =============================================
|
| 28 |
+
TICKER = '222080'
|
| 29 |
|
| 30 |
# =============================================
|
| 31 |
+
# ์ค์
|
| 32 |
# =============================================
|
| 33 |
+
VOL_MA_PERIOD = 20
|
| 34 |
+
VOL_CONTRACT_THR = 0.8 # ์์ถ ๊ธฐ์ค (20์ผMA ร 0.8 ์ดํ)
|
| 35 |
+
VOL_EXPLODE_THR = 2.0 # ํญ๋ฐ ๊ธฐ์ค (20์ผMA ร 2.0 ์ด์)
|
| 36 |
+
MIN_PRICE_GAIN = 0.03 # ๋ํ์ผ ์ต์ ์์น๋ฅ (3%)
|
| 37 |
+
MIN_CONTRACT_DAYS = 2 # ์ต์ ์์ถ ์ผ์
|
| 38 |
+
EXPLODE_WINDOW = 5 # ์์ถ ํ N์ผ ๋ด ๋ํ ํ์ธ
|
| 39 |
+
DATA_START = (_now_kst() - timedelta(days=400)).strftime('%Y-%m-%d')
|
| 40 |
+
ANALYZE_FROM = (_now_kst() - timedelta(days=365)).strftime('%Y-%m-%d')
|
| 41 |
+
ANALYZE_TO = _now_kst().strftime('%Y-%m-%d') # ์ค๋(KST) โ ๋งค ์คํ์ ์ต์
|
| 42 |
|
| 43 |
# =============================================
|
| 44 |
+
# ๋ฐ์ดํฐ ๋ก๋
|
| 45 |
# =============================================
|
| 46 |
+
print("=" * 65)
|
| 47 |
+
print(f"VCP ๋ถ์: {TICKER} | ๊ธฐ๊ฐ: {ANALYZE_FROM} ~ {ANALYZE_TO}")
|
| 48 |
+
print("=" * 65)
|
|
|
|
|
|
|
| 49 |
|
| 50 |
+
df = fdr.DataReader(TICKER, DATA_START, ANALYZE_TO)
|
| 51 |
+
if df is None or len(df) == 0:
|
| 52 |
+
print("๋ฐ์ดํฐ ์์"); sys.exit()
|
| 53 |
|
| 54 |
+
df['vol_ma20'] = df['Volume'].rolling(VOL_MA_PERIOD).mean()
|
| 55 |
+
df['vol_contract'] = df['Volume'] < df['vol_ma20'] * VOL_CONTRACT_THR
|
| 56 |
+
df['price_range'] = df['High'] - df['Low']
|
| 57 |
+
df['range_ma10'] = df['price_range'].rolling(10).mean()
|
| 58 |
+
df['prev_close'] = df['Close'].shift(1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 59 |
|
| 60 |
+
print(f"๋ฐ์ดํฐ: {len(df)}์ผ ๋ก๋ ์๋ฃ\n")
|
| 61 |
|
| 62 |
# =============================================
|
| 63 |
+
# VCP ๊ฐ์ง
|
| 64 |
# =============================================
|
| 65 |
+
vcps = []
|
| 66 |
+
i = VOL_MA_PERIOD
|
| 67 |
+
|
| 68 |
+
while i < len(df) - 1:
|
| 69 |
+
if not df['vol_contract'].iloc[i]:
|
| 70 |
+
i += 1; continue
|
| 71 |
+
|
| 72 |
+
# ์์ถ ๊ตฌ๊ฐ ์ฐพ๊ธฐ
|
| 73 |
+
start = i
|
| 74 |
+
while start > VOL_MA_PERIOD and df['vol_contract'].iloc[start - 1]:
|
| 75 |
+
start -= 1
|
| 76 |
+
end = i
|
| 77 |
+
while end < len(df) - 1 and df['vol_contract'].iloc[end + 1]:
|
| 78 |
+
end += 1
|
| 79 |
+
|
| 80 |
+
days = end - start + 1
|
| 81 |
+
if days < MIN_CONTRACT_DAYS:
|
| 82 |
+
i = end + 1; continue
|
| 83 |
+
|
| 84 |
+
zone = df.iloc[start:end+1]
|
| 85 |
+
zone_low = float(zone['Low'].min())
|
| 86 |
+
zone_high = float(zone['High'].max())
|
| 87 |
+
zone_vol = float(zone['Volume'].mean())
|
| 88 |
+
vol_ma = float(df['vol_ma20'].iloc[end])
|
| 89 |
+
vol_ratio = round(zone_vol / vol_ma, 2) if vol_ma > 0 else 0
|
| 90 |
+
|
| 91 |
+
# ๊ฐ๊ฒฉ ์์ถ ํ์ธ
|
| 92 |
+
pre_range_ma = float(df['range_ma10'].iloc[max(0, start-1)])
|
| 93 |
+
zone_range = float(zone['price_range'].mean())
|
| 94 |
+
is_contracting = (zone_range < pre_range_ma) if pre_range_ma > 0 else False
|
| 95 |
+
|
| 96 |
+
# ๋ํ ํ์ธ (4๊ฐ์ง ์กฐ๊ฑด ๋ชจ๋)
|
| 97 |
+
breakout = False
|
| 98 |
+
breakout_date = None
|
| 99 |
+
breakout_price = None
|
| 100 |
+
breakout_vol_r = None
|
| 101 |
+
breakout_gain = None
|
| 102 |
+
fail_reason = None
|
| 103 |
+
|
| 104 |
+
for j in range(end + 1, min(end + EXPLODE_WINDOW + 1, len(df))):
|
| 105 |
+
close_j = float(df['Close'].iloc[j])
|
| 106 |
+
open_j = float(df['Open'].iloc[j])
|
| 107 |
+
vol_j = float(df['Volume'].iloc[j])
|
| 108 |
+
vol_ma_j = float(df['vol_ma20'].iloc[j])
|
| 109 |
+
prev_close = float(df['prev_close'].iloc[j])
|
| 110 |
+
|
| 111 |
+
if prev_close == 0 or pd.isna(prev_close):
|
| 112 |
+
continue
|
| 113 |
|
| 114 |
+
price_gain = (close_j - prev_close) / prev_close # ์ ์ผ ๋๋น ์์น๋ฅ
|
| 115 |
+
is_bullish = close_j > open_j # ์๋ด
|
| 116 |
+
price_break = close_j > zone_high # ๊ตฌ๊ฐ ๊ณ ๊ฐ ๋ํ
|
| 117 |
+
vol_break = vol_j > vol_ma_j * VOL_EXPLODE_THR # ๊ฑฐ๋๋ ํญ๋ฐ
|
| 118 |
+
gain_ok = price_gain >= MIN_PRICE_GAIN # 3% ์ด์ ์์น
|
| 119 |
+
|
| 120 |
+
if price_break and vol_break and gain_ok and is_bullish:
|
| 121 |
+
breakout = True
|
| 122 |
+
breakout_date = str(df.index[j])[:10]
|
| 123 |
+
breakout_price = close_j
|
| 124 |
+
breakout_vol_r = round(vol_j / vol_ma_j, 2)
|
| 125 |
+
breakout_gain = round(price_gain * 100, 1)
|
| 126 |
+
break
|
| 127 |
+
elif price_break:
|
| 128 |
+
# ๊ฐ๊ฒฉ์ ๋ํํ์ง๋ง ๋ค๋ฅธ ์กฐ๊ฑด ๋ฏธ์ถฉ์กฑ โ ์ด์ ๊ธฐ๋ก
|
| 129 |
+
reasons = []
|
| 130 |
+
if not vol_break:
|
| 131 |
+
reasons.append(f"๊ฑฐ๋๋ ๋ถ์กฑ({round(vol_j/vol_ma_j,1)}x, ํ์ {VOL_EXPLODE_THR}x)")
|
| 132 |
+
if not gain_ok:
|
| 133 |
+
reasons.append(f"์์น๋ฅ ๋ถ์กฑ({price_gain*100:+.1f}%, ํ์ {MIN_PRICE_GAIN*100:.0f}%+)")
|
| 134 |
+
if not is_bullish:
|
| 135 |
+
reasons.append("์๋ด")
|
| 136 |
+
fail_reason = str(df.index[j])[:10] + " ๊ฐ๊ฒฉ๋ํํ์ง๋ง: " + ", ".join(reasons)
|
| 137 |
+
|
| 138 |
+
vcp = {
|
| 139 |
+
'start_date': str(df.index[start])[:10],
|
| 140 |
+
'end_date': str(df.index[end])[:10],
|
| 141 |
+
'days': days,
|
| 142 |
+
'zone_low': round(zone_low, 0),
|
| 143 |
+
'zone_high': round(zone_high, 0),
|
| 144 |
+
'vol_ratio': vol_ratio,
|
| 145 |
+
'is_contracting': is_contracting,
|
| 146 |
+
'zone_range': round(zone_range, 0),
|
| 147 |
+
'pre_range_ma': round(pre_range_ma, 0),
|
| 148 |
+
'confirmed': breakout,
|
| 149 |
+
'breakout_date': breakout_date,
|
| 150 |
+
'breakout_price': breakout_price,
|
| 151 |
+
'breakout_vol_r': breakout_vol_r,
|
| 152 |
+
'breakout_gain': breakout_gain,
|
| 153 |
+
'fail_reason': fail_reason,
|
| 154 |
+
}
|
| 155 |
+
vcps.append(vcp)
|
| 156 |
+
i = end + 1
|
| 157 |
+
|
| 158 |
+
# ๋ถ์ ๊ธฐ๊ฐ ํํฐ
|
| 159 |
+
vcps = [v for v in vcps if v['end_date'] >= ANALYZE_FROM]
|
| 160 |
|
| 161 |
# =============================================
|
| 162 |
+
# ๊ฒฐ๊ณผ ์ถ๋ ฅ
|
| 163 |
# =============================================
|
| 164 |
+
print(f"๊ฐ์ง๋ VCP: {len(vcps)}๊ฐ\n")
|
| 165 |
+
|
| 166 |
+
if len(vcps) == 0:
|
| 167 |
+
print("VCP ํจํด ์์"); sys.exit()
|
| 168 |
+
|
| 169 |
+
for idx, v in enumerate(vcps, 1):
|
| 170 |
+
status = "[ํ์ ] " if v['confirmed'] else "[๋ฏธํ์ ]"
|
| 171 |
+
print(f"{'='*65}")
|
| 172 |
+
print(f"VCP #{idx} {status} {v['start_date']} ~ {v['end_date']} ({v['days']}์ผ)")
|
| 173 |
+
print(f"{'='*65}")
|
| 174 |
+
print(f" ์์ถ๊ตฌ๊ฐ: {v['zone_low']:,.0f} ~ {v['zone_high']:,.0f}์")
|
| 175 |
+
print(f" ๊ฑฐ๋๋: 20์ผMA ๋๋น {v['vol_ratio']*100:.0f}% (๊ธฐ์ค {VOL_CONTRACT_THR*100:.0f}% ์ดํ)")
|
| 176 |
+
|
| 177 |
+
if v['pre_range_ma'] > 0:
|
| 178 |
+
shrink = (1 - v['zone_range'] / v['pre_range_ma']) * 100
|
| 179 |
+
mark = 'O' if v['is_contracting'] else 'X'
|
| 180 |
+
print(f" ๊ฐ๊ฒฉ์์ถ: {mark} (๊ตฌ๊ฐ {v['zone_range']:,.0f} vs ์ง์ MA {v['pre_range_ma']:,.0f}, {shrink:+.0f}%)")
|
| 181 |
+
|
| 182 |
+
if v['confirmed']:
|
| 183 |
+
print(f" ๋ํ: {v['breakout_date']} | "
|
| 184 |
+
f"์ข
๊ฐ {v['breakout_price']:,.0f}์ | "
|
| 185 |
+
f"์์น๋ฅ +{v['breakout_gain']}% | "
|
| 186 |
+
f"๊ฑฐ๋๋ MAร{v['breakout_vol_r']} | ์๋ด")
|
| 187 |
+
elif v['fail_reason']:
|
| 188 |
+
print(f" ๋ํ์๋: {v['fail_reason']}")
|
| 189 |
+
else:
|
| 190 |
+
print(f" ๋ํ: ์์")
|
| 191 |
|
| 192 |
+
print(f"\n [ํ๋จ ์ด์ ]")
|
| 193 |
+
print(f" - ๊ฑฐ๋๋ {v['days']}์ผ ์ฐ์ 20์ผMAร{VOL_CONTRACT_THR} ์ดํ ์์ถ")
|
| 194 |
+
print(f" - ๊ฐ๊ฒฉ์์ถ: {'ํ์ธ' if v['is_contracting'] else '๋ฏธํ์ธ'}")
|
| 195 |
+
if v['confirmed']:
|
| 196 |
+
print(f" - ๊ตฌ๊ฐ๊ณ ๊ฐ({v['zone_high']:,.0f}) ๋ํ + ์์น๋ฅ {v['breakout_gain']}% + ๊ฑฐ๋๋ MAร{v['breakout_vol_r']} + ์๋ด โ ํ์ ")
|
| 197 |
+
else:
|
| 198 |
+
print(f" - 4๊ฐ์ง ๋ํ์กฐ๊ฑด ๋ฏธ์ถฉ์กฑ (๊ฐ๊ฒฉ๋ํ+3%์์น+๊ฑฐ๋๋2x+์๋ด)")
|
| 199 |
print()
|
| 200 |
|
| 201 |
+
# ์์ฝ
|
| 202 |
+
confirmed = [v for v in vcps if v['confirmed']]
|
| 203 |
+
pending = [v for v in vcps if not v['confirmed']]
|
| 204 |
+
|
| 205 |
+
print("=" * 65)
|
| 206 |
+
print("์ต์ข
์์ฝ")
|
| 207 |
+
print("=" * 65)
|
| 208 |
+
print(f"์ ์ฒด: {len(vcps)}๊ฐ | ํ์ : {len(confirmed)}๏ฟฝ๏ฟฝ | ๋ฏธํ์ : {len(pending)}๊ฐ")
|
| 209 |
+
|
| 210 |
+
if confirmed:
|
| 211 |
+
print("\n[ํ์ VCP]")
|
| 212 |
+
for v in confirmed:
|
| 213 |
+
print(f" {v['start_date']}~{v['end_date']} | "
|
| 214 |
+
f"๊ตฌ๊ฐ {v['zone_low']:,.0f}~{v['zone_high']:,.0f} | "
|
| 215 |
+
f"๋ํ {v['breakout_date']} +{v['breakout_gain']}% MAร{v['breakout_vol_r']}")
|
| 216 |
+
|
| 217 |
+
if pending:
|
| 218 |
+
print("\n[๋ฏธํ์ VCP]")
|
| 219 |
+
for v in pending:
|
| 220 |
+
print(f" {v['start_date']}~{v['end_date']} | "
|
| 221 |
+
f"๊ตฌ๊ฐ {v['zone_low']:,.0f}~{v['zone_high']:,.0f}")
|