20251220

Python

import pandas as pd
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
import numpy as np

# Load the HTML file
file_path = 'CRO_Report_28026_Classic_Microstructure_&_Cost_Analysis.html'
with open(file_path, 'r', encoding='utf-8') as f:
    html_content = f.read()

soup = BeautifulSoup(html_content, 'html.parser')

# Extract Spot, DTE, Timestamp
spot_text = soup.find('span', string=lambda x: x and 'Spot:' in x).get_text()
spot = float(spot_text.split(':')[1].strip())

dte_text = soup.find('span', string=lambda x: x and 'DTE:' in x).get_text()
dte = dte_text.split(' ')[0].split(':')[1].strip()

timestamp = soup.find('span', style=lambda x: x and 'color:#666' in x).get_text().strip()

# Extract Spectrum Matrix Table
table = soup.find('table')
headers = [th.text.strip() for th in table.find_all('th')]
rows = []
for tr in table.find_all('tr')[1:]:
    cells = [td.text.strip().replace(',', '') for td in tr.find_all('td')]
    if cells:
        rows.append(cells)

df = pd.DataFrame(rows, columns=headers)
df['Strike'] = pd.to_numeric(df['Strike'])
df['NetAcc'] = pd.to_numeric(df['NetAcc'])
df['NetFlow'] = pd.to_numeric(df['NetFlow'])
df['AvgC'] = pd.to_numeric(df['AvgC'])
df['AvgP'] = pd.to_numeric(df['AvgP'])

# Extract Gamma Risk Table
gamma_table = soup.find_all('table')[1]
gamma_headers = [th.text.strip() for th in gamma_table.find_all('th')]
gamma_rows = []
for tr in gamma_table.find_all('tr')[1:]:
    cells = [td.text.strip().replace(',', '') for td in tr.find_all('td')]
    if cells:
        gamma_rows.append(cells)
df_gamma = pd.DataFrame(gamma_rows, columns=gamma_headers)

# Plotting - Terrain Geometry Chart
# Filter strikes for "逢百" (every 100) labels but use all data for bars
all_strikes = df['Strike'].unique()
hundred_strikes = [s for s in all_strikes if s % 100 == 0]

plt.rcParams['font.sans-serif'] = ['DejaVu Sans'] # Using default
plt.rcParams['axes.unicode_minus'] = False

# Setup plot
fig, ax = plt.subplots(figsize=(12, 6))

# NetAcc Bars
acc_colors = ['lightcoral' if x > 0 else 'lightgreen' for x in df['NetAcc']] # lightred/lightgreen
ax.bar(df['Strike'], df['NetAcc'], width=40, color=acc_colors, alpha=0.5, label='NetAcc')

# NetFlow Bars (overlay)
flow_colors = ['darkred' if x > 0 else 'darkgreen' for x in df['NetFlow']]
ax.bar(df['Strike'], df['NetFlow'], width=20, color=flow_colors, alpha=0.8, label='NetFlow')

# Spot line
ax.axvline(x=spot, color='black', linestyle='--', linewidth=1.5)
ax.text(spot + 10, ax.get_ylim()[1] * 0.9, f'Spot: {spot}', fontweight='bold')

# Labels and Ticks
ax.set_xticks(hundred_strikes)
ax.set_xticklabels([str(int(s)) for s in hundred_strikes], rotation=45)
ax.set_ylabel('Volume / Force')
ax.legend()

plt.tight_layout()
plt.savefig('terrain_geometry_chart.png')

# Output some key data for the report
print(f"Spot: {spot}, DTE: {dte}, Time: {timestamp}")
print(df[df['Strike'].between(spot-200, spot+200)])

程式碼輸出內容

Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'bs4'

Python

import pandas as pd
import matplotlib.pyplot as plt
import re

# Read the HTML file content
file_path = 'CRO_Report_28026_Classic_Microstructure_&_Cost_Analysis.html'
with open(file_path, 'r', encoding='utf-8') as f:
    content = f.read()

# Extract Spot using regex
spot_match = re.search(r'Spot:</strong>\s*(\d+)', content)
spot = float(spot_match.group(1)) if spot_match else 28026.0

# Extract DTE using regex
dte_match = re.search(r'DTE:</strong>\s*(\d+)', content)
dte = dte_match.group(1) if dte_match else "4"

# Extract timestamp
time_match = re.search(r'(\d{4}/\d{2}/\d{2} \d{2}:\d{2})', content)
timestamp = time_match.group(1) if time_match else "Unknown"

# Extract tables
tables = pd.read_html(file_path)
df_spectrum = tables[0]
df_gamma = tables[1]

# Clean df_spectrum
# Columns: Strike, NetAcc, Str(%), NetFlow, Flow(%), Verdict, Risk Note, AvgC, AvgP
# Remove commas and convert to numeric
for col in ['Strike', 'NetAcc', 'NetFlow', 'AvgC', 'AvgP']:
    df_spectrum[col] = df_spectrum[col].astype(str).str.replace(',', '').str.replace('+', '').astype(float)

# Plotting
fig, ax = plt.subplots(figsize=(12, 7))

# NetAcc Bars (light colors)
acc_colors = ['#ffcccc' if x > 0 else '#ccffcc' for x in df_spectrum['NetAcc']] # lightred/lightgreen
ax.bar(df_spectrum['Strike'], df_spectrum['NetAcc'], width=40, color=acc_colors, label='NetAcc', zorder=2)

# NetFlow Bars (darker colors, overlay)
flow_colors = ['#8b0000' if x > 0 else '#006400' for x in df_spectrum['NetFlow']] # darkred/darkgreen
ax.bar(df_spectrum['Strike'], df_spectrum['NetFlow'], width=20, color=flow_colors, label='NetFlow', zorder=3)

# Spot line
ax.axvline(x=spot, color='black', linestyle='--', linewidth=2, zorder=4)
ax.text(spot, ax.get_ylim()[1] * 0.95, f' Spot: {int(spot)}', fontsize=12, fontweight='bold', color='black')

# Formatting X-axis: 逢百 (every 100)
all_strikes = df_spectrum['Strike'].unique()
hundred_strikes = sorted([s for s in all_strikes if s % 100 == 0])
ax.set_xticks(hundred_strikes)
ax.set_xticklabels([str(int(s)) for s in hundred_strikes], rotation=45)

# Axis labels
ax.set_xlabel('Strike')
ax.set_ylabel('Volume (Force)')
ax.grid(axis='y', linestyle=':', alpha=0.6)
ax.legend()

plt.tight_layout()
plt.savefig('terrain_geometry_chart.png')

# Print core data for analysis
print(f"Spot: {spot}, DTE: {dte}, Time: {timestamp}")
# Look at strikes around Spot
around_spot = df_spectrum[(df_spectrum['Strike'] >= 27800) & (df_spectrum['Strike'] <= 28200)]
print(around_spot[['Strike', 'NetAcc', 'NetFlow', 'AvgC', 'AvgP']])
# Look at Ironclad
ironclad = df_spectrum[df_spectrum['NetAcc'].abs() == df_spectrum['NetAcc'].abs().max()]
print("Ironclad Support/Pressure:")
print(ironclad[['Strike', 'NetAcc', 'NetFlow']])

程式碼輸出內容

Spot: 28026.0, DTE: 4, Time: 2025/12/20 09:12
     Strike  NetAcc  NetFlow   AvgC   AvgP
13  27800.0  1324.0   1323.0  215.4  134.4
14  27850.0  -351.0    -78.0  206.5  180.0
15  27900.0   399.0    745.0  205.0  307.9
16  27950.0  -431.0   -208.0  132.5  191.5
17  28000.0   854.0    911.0  107.4  184.2
18  28050.0  -106.0     25.0  106.6  219.7
19  28100.0  -462.0   -236.0  105.1  247.9
20  28150.0  -291.0      3.0   73.5  264.4
21  28200.0  -466.0    197.0   47.5  306.1
Ironclad Support/Pressure:
    Strike  NetAcc  NetFlow
5  27400.0  2340.0   1982.0

📈 市場微結構數據運算報告 (CRO 風險視角)

運算中心價格 (Spot Price): 28,026

DTE (距結算日): 4

時間戳記: 2025/12/20 09:12

風險狀態：結構性斷層風險。現價 (28,026) 與核心 Gamma 支撐帶 (27,400 - 27,800) 存在顯著空間脫節。雖然 28,000 暫提供結構支撐，但上方 28,100 處壓力已現，且流動性在 28,050 附近出現真空。

1. 🔥 全頻譜分級表 (The Full Spectrum Table)

CRO 聲明：此表揭露市場真實骨架，包含被大眾忽略的 "Ghost Zones" (流動性真空區)。

Strike	NetAcc	Str(%)	NetFlow	Flow(%)	Verdict	Risk Note
27400	2,340	100%	1,982	100%	IRONCLAD	Major Support
27700	1,317	56%	1,268	64%	MODERATE	Double Support
27800	1,324	57%	1,323	67%	MODERATE	Double Support
27900	399	17%	745	38%	WEAK	Reversal Pivot
28000	854	36%	911	46%	MODERATE	Double Support
28050	-106	5%	25	1%	WEAK	Ghost Zone
28100	-462	20%	-236	12%	WEAK	Double Pressure
28150	-291	12%	3	0%	WEAK	Ghost Zone
28200	-466	20%	197	10%	WEAK	Reversal Pivot

2. 結構性偏斜與核心阻力分析 (Structural Skew & Resistance)

整體籌碼呈現「底部重、頂部輕」的非對稱結構。

• 成本鑑識 (Cost Basis Forensics)：

  • 28,000 Call 莊家防線： 平均成本 107.4，損益平衡點為 28,107。現價 28,026 尚未觸及賣方痛點，軋空動能尚未引發。

  • 28,000 Put 莊家防線： 平均成本 184.2，防禦點為 27,816。這代表賣方在 28,000 支撐處有極厚的緩衝空間。

• 深層流向悖論 (Deep Flow Paradox)：

  • 注意 27,400 (Ironclad) 與 27,350 (Pressure) 的對沖陷阱。雖然遠離現價，但 27,400 聚集了全場最強的 Put 賣方 (NetAcc +2,340)，而 27,350 卻出現負向流量 (-1,167)。此為典型的「大壩懸崖」，一旦極端行情回測 27,400 失敗，下方將無支撐。

3. 結構性支撐與測試點 (Structural Support & Testing Points)

等級	履約價 (Strike)	NetAcc (累積結構)	NetFlow (當日流量)	結構本質
即時支撐	28,000	+854 (36%)	+911 (強烈買流)	Moderate Floor
核心引力	27,800	+1,324 (57%)	+1,323 (強烈買流)	Structural Base
遠端鐵底	27,400	+2,340 (100%)	+1,982 (Max Flow)	IRONCLAD Wall

4. 風險能量計算 (Gamma Risk Distribution)

Gamma Fuel 用於衡量潛在的波動率爆炸能量。

排名	履約價 (Strike)	Gamma Fuel	AccQtyC	AccQtyP	風險解讀
1	27,400	2,509	-84	-2,425	Put Floor (絕對防禦)
2	27,800	2,251	-464	-1,787	Put Floor (二級支撐)
3	27,700	2,162	-423	-1,740	Put Floor (二級支撐)

5. Market Terrain 定義 (Final State Definition)

當前狀態： 多頭推進中的「流動性斷層」。雖然 28,000 具備當日流量支持，但 28,050 - 28,150 區間屬於 Ghost Zones (幽靈真空)。

風險路徑 (Potential Paths):

1. 幽靈真空滑行： 若價格突破 28,050，由於上方缺乏結構壓制 (28,100 僅 -462)，價格可能快速滑向 28,100 甚至測試 28,200。

2. 回撤測試： 若 28,000 關卡失守，下方 27,900 支撐微弱，價格將快速下探 27,816 (28,000 Put 莊家防線) 尋找支撐。

操作建議 (Trade-Offs Only):

• 多頭 (Long): 28,000 具備 Moderate 支撐特質。若回測 28,000 不破，可利用 28,050 之後的真空區賺取快速點數，目標 28,100。

• 空頭 (Short): 28,100 之前不宜強行逆勢。較佳點位在 28,107 (Call 莊家防線) 附近觀察動能衰竭。

• 中性 (Neutral): 嚴禁在 28,000 執行無保護賣出。考慮到 Gamma Fuel 集中在下方，保護腿必須配置在 27,800 以下。

CRO 警告： 28,050 存在明顯的結構斷層。價格在此區域的 Delta 變動將不具備線性特徵，波動可能突發性放大。

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