#!/usr/bin/env python
# coding: utf-8

# # K線 反轉型態 - 多頭吞噬/空頭吞噬型態 

# # Part 0: Import 套件

# - TA-Lib: https://mrjbq7.github.io/ta-lib/install.html
# - plotly: https://plotly.com/python/
# - yfinance: https://pypi.org/project/yfinance/

# In[2]:


#財經分析套件
import talib

# 下載資料套件
import yfinance as yf
import requests as r

#資料處理套件
import pandas as pd
import json
from datetime import datetime, date

# 畫圖套件
import plotly.graph_objects as go


# 如果沒有上述套件 可以用pip install 或 conda install 指令安裝
# 1. pip install
#     - pip install plotly
#     - pip install TA-Lib
#     - pip install yfinance
# 2. conda install
#     - conda install -c plotly plotly
#     - conda install -c conda-forge ta-lib

# # Part 1: 在台股個股Ｋ線圖上，標示出 多頭吞噬/空頭吞噬 Ｋ線型態

# ## 1.1 下載台股個股歷史走勢資料
# 
# - 股海小英雄youtube影片：[【Python 爬蟲】台股個股資料免費下載｜選擇想要的個股資料！](https://youtu.be/_4CEymrQbV8)
# - 資料來源：[證交所 個股日成交資訊](https://www.twse.com.tw/zh/page/trading/exchange/STOCK_DAY.html)

# In[3]:


def get_tw_stock_data(start_year, start_month, end_year, end_month, stock_code):
    start_date = str(date(start_year, start_month, 1))
    end_date = str(date(end_year, end_month, 1))
    month_list = pd.date_range(start_date, end_date, freq='MS').strftime("%Y%m%d").tolist()
    
    df = pd.DataFrame()
    for month in month_list:
        url = "https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date="+ month + "&stockNo=" + str(stock_code)
        res = r.get(url)
        stock_json = res.json()
        stock_df = pd.DataFrame.from_dict(stock_json['data'])
        df = df.append(stock_df, ignore_index = True)
        
    # 資料轉型
    for col in [0, 1, 2, 3, 4, 5, 6, 8]:
        for row in range(df.shape[0]):
            # 把"日期"從字串(string)換成時間(datetime)，並將民國年換成西元年
            if col == 0:
                day = df.iloc[row,0].split('/')
                df.iloc[row, 0] = datetime(int(day[0]) + 1911, int(day[1]), int(day[2]))  
            # 把"開盤價", "最高價", "最低價", "收盤價"帶有逗號的字串(string)換成浮點數(float) 
            elif col != 0:
                df.iloc[row, col] = float(df.iloc[row,col].replace(',', ''))
    
    df.columns = ['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數']
    return df


# In[4]:


#下載台股長榮海運:2603
df = get_tw_stock_data(start_year = 2021, 
                       start_month = 6, 
                       end_year = 2021, 
                       end_month = 8, 
                       stock_code = 2603)
df.head()


# ## 1.2: 用Ta-Lib套件，找出 多頭吞噬/空頭吞噬 K線型態
# - [Ta-Lib K線型態介紹](https://www.quantshare.com/index.php?option=manual&dir=/QuantShare%20Language/Candlestick%20Pattern)

# ### Ta-Lib支援的K線型態們

# In[5]:


print(talib.get_function_groups()['Pattern Recognition'])


# ### Ta-Lib K線型態函式名稱
# - 吞噬: 'CDLENGULFING'
# 
# ### Ta-Lib K線型態函式回傳值
# - 偏上漲型態: +100
# - 無此型態: 0
# - 偏下跌型態: -100

# ### 計算 吞噬 型態

# In[6]:


# 使用ta-lib吞噬型態函式
candle_names = ['CDLENGULFING']

# 加K線型態的欄位 create columns for  pattern
for candle in candle_names:
    df[candle] = getattr(talib, candle)(df['開盤價'], df['最高價'], df['最低價'], df['收盤價'])

df.head()


# ### 找出 吞噬: 'CDLENGULFING' 型態
# - 'CDLENGULFING' 函式回傳值 **100**: 多頭吞噬，偏上漲型態
# - 'CDLENGULFING' 函式回傳值 **-100**: 空頭吞噬，偏下跌型態

# In[7]:


idx = df[df['CDLENGULFING']!= 0].index
df[df['CDLENGULFING']!= 0]


# ## 1.3: 在台股個股Ｋ線圖上，標示出吞噬Ｋ線型態
# - 股海小英雄youtube影片：[【股市分析】用K線預測大盤趨勢](https://youtu.be/FX6YAyOdEaE)

# In[13]:


# 畫大盤Ｋ線圖
fig = go.Figure(data = [go.Candlestick(x = df['日期'],
                open = df['開盤價'],
                high = df['最高價'],
                low = df['最低價'],
                close = df['收盤價'],
                increasing_line_color = 'red', 
                decreasing_line_color = 'green')])
# 標示吞噬型態
for i in range(len(idx)):
    day = df.iloc[idx[i], 0]
    direction = '多頭' if df.iloc[idx[i], -1] > 0 else '空頭'
    high = float(df[df['日期'] == day]['最高價'])
    fig.add_annotation(x = day, 
                       y = high + 1,
                       opacity = 0.9,
                       font_size = 15,
                       text = direction + "吞噬 ",
                       showarrow = True,
                       arrowhead = 1)
    
# 設圖標及圖長寬
fig.update_layout(
    title_text = "2021/06 - 2021/08 長榮海運(2603) K線圖 - 多頭吞噬/空頭吞噬型態",
    width = 1000,
    height = 600
)
# 設x軸標題
fig.update_xaxes(title_text = "日期")

# 設y軸標題
fig.update_yaxes(title_text = "股價")

fig.show()


# # Part 2: 在陸股個股Ｋ線圖上，標示出 多頭吞噬/空頭吞噬 Ｋ線型態

# ## 2.1 下載陸股個股歷史走勢資料
# - 股海小英雄youtube影片：[【Python 股票】美股個股資料免費下載｜用Yahoo財經套件(yfinance)取得資料｜擺脫限制](https://youtu.be/ZIFmDbIb-ro)

# In[14]:


# 下載A股貴州茅台：600519.SS
stock = yf.download("600519.SS", "2021-02-05", "2021-06-06")
stock.head()


# ## 2.2: 用Ta-Lib套件，找出 多頭吞噬/空頭吞噬 K線型態
# ### 計算 吞噬 型態

# In[15]:


# 使用ta-lib吞噬型態函式
df = stock
candle_names = ['CDLENGULFING']

# 加各種K線型態的欄位 create columns for  pattern
for candle in candle_names:
    df[candle] = getattr(talib, candle)(df['Open'], df['High'], df['Low'], df['Close'])

df.head()


# ### 找出 吞噬: 'CDLENGULFING' 型態
# - 'CDLENGULFING' 函式回傳值 **100**: 多頭吞噬，偏上漲型態
# - 'CDLENGULFING' 函式回傳值 **-100**: 空頭吞噬，偏下跌型態

# In[16]:


idx = df[df['CDLENGULFING']!= 0].index
df[df['CDLENGULFING']!= 0]


# ## 2.3: 在陸股個股Ｋ線圖上，標示出吞噬Ｋ線型態
# - 股海小英雄youtube影片：[【股市分析】用K線預測大盤趨勢](https://youtu.be/FX6YAyOdEaE)

# In[19]:


# 畫大盤Ｋ線圖
fig = go.Figure(data = [go.Candlestick(x = df.index,
                open = df['Open'],
                high = df['High'],
                low = df['Low'],
                close = df['Close'],
                increasing_line_color = 'red', 
                decreasing_line_color = 'green')])
# 標示吞噬型態
for i in range(len(idx)):
    day = idx[i]
    direction = '多頭' if df.loc[day][-1] > 0 else '空頭'
    high = df.loc[day][1]
    fig.add_annotation(x = day, 
                       y = high + 1,
                       opacity = 0.9,
                       font_size = 15,
                       text = direction + "吞噬 ",
                       showarrow = True,
                       arrowhead = 1)
    
# 設圖標及圖長寬
fig.update_layout(
    title_text = "2021/02 - 2021/06 貴州茅台(600519) K線圖 - 多頭吞噬/空頭吞噬型態",
    width = 1000,
    height = 600
)
# 設x軸標題
fig.update_xaxes(title_text = "日期")

# 設y軸標題
fig.update_yaxes(title_text = "股價")

fig.show()